Recent Advances in Simulated Evolution and Learning

The following sections are included:

• PREFACE

• TABLE OF CONTENTS

An interesting problem is under what circumstances will a collection of interacting agents realize efficient collective actions. This question will depend crucially on how self-interested agents interact and how they learn from each other. We model strategic interactions as dilemma games, coordination games or hawk-dove games. It is well known that the replicator dynamics based on natural selection converge to an inefficient equilibrium. In this chapter, we focus on the effect of coevolutionary learning. Each agent is modeled to learn interaction rules defined as the function of own strategy and the strategy of the neighbor. We show that a collection of interacting agents converges into equilibrium in which the conditions of efficiency and equity are satisfied. We investigate interaction rules acquired by all agents and show that they share several rules with the common features to sustain equitable social efficiency. This chapter also presents a comparative study of two evolving populations, one in a spatial environment, and the other in a small-world environment. The effect of the environment on the emergence of social efficiency is studied. The small-world environment is shown to encourage the emergence of social efficiency further than the spatial structure.

Recommender systems are new types of internet-based software tools, designed to help users find their way through today's complex on-line shops and entertainment websites. This chapter describes a new recommender system, which employs a genetic algorithm to learn personal preferences of users and provide tailored suggestions.

Parallelization of genetic algorithms (GAs) has received considerable attention in recent years. Reasons for this are the availability of suitable computational resources and the need for solving harder problems in reasonable time. We describe a new parallel self-adaptive GA for solving the data clustering problem. The algorithm utilizes island parallelization using a genebank model, in which GA processes communicate with each other through the genebank process. This model allows one to implement different migration topologies in an easy manner. Experiments show that significant speedup is reached by parallelization. The effect of migration parameters is also studied and the development of population diversity is examined by several measures, some of which are new.

In this chapter we study the effects of representing a traditional portfolio optimization problem as a classification task in order to reduce the computational cost, and finding more reliable solutions. We use N-Version Genetic Programming to represent the market as a binary classification problem, and evolve two trading strategies that independently look for either buy, or sell, opportunities in parallel. The system is made more fault-tolerant using majority voting for the investment decisions. As inputs to our system we use a large number of instruments from technical analysis, which allows us to increase the execution speed over 100 times using a Sub-Machine-Code Genetic Programming system that evaluates 128 fitness cases in parallel. We see that the strategies generalize well and outperform the buy-and-hold strategy on simulated out-of-sample trading, so there is a clear connection between good classification results and returns on trading. We also see that the n-version voting system can successfully be used to reduce risk. Finally we see that some of the technical analysis instruments appear more frequently than others in the most successful strategies, which could be an indication on actual correlations to the future share price.

A coevolutionary algorithm is an extension of the conventional genetic algorithm that incorporates the strategy of divide and conquer in developing a complex solution in the form of interacting co-adapted subcomponents. It takes advantage of the reduced search space by evolving species associated with subsets of variables independently but cooperatively. In this chapter we propose an efficient coevolutionary algorithm combining species splitting and merging together. Our algorithm conducts efficient local search in the reduced search space by splitting species for independent variables while it conducts global search by merging species for interdependent variables. We have experimented the proposed algorithm with several benchmarking function optimization problems and the inventory control problem, and have shown that the algorithm outperforms existing coevolutionary algorithms.

A method has been developed to derive an evolution equation of schemata under the action of genetic operators. The method makes use of the fact that schema frequencies can be given by Walsh transformation of genotype frequencies. It is applied to genetic algorithms (GAs) on the multiplicative landscape. On this landscape, an exact evolution equation for the first order schemata can be derived within the framework of an infinite population model, and this makes it possible to carry out an analytical investigation of genetic operators. The theoretical results are compared with numerical experiments. The analysis of the experiments focuses on the interplay of mutation and crossover, and investigates the effect of linkage due to finite population size.

This chapter describes the incorporation of an adaptive learning model to the framework we have designed to implement artificial life characters. This model is based on language structures representing actions and strategies developed by these artificial characters to solve their problems. A set of problems related to how characters evolve and learn may be studied here, ranging from basic survival in their environment to the emergence of knowledge exchange supported by the usage of language to communicate ideas. Furthermore, it presents also a virtual reality application that has been implemented at the USP Digital CAVE. It is an aquarium inhabited by artificial fishes that are able to evolve in this environment using their learning ability. These fishes have their own cognition, which control their actions – mainly swimming and eating. Through contact and communication with other fishes they learn how to behave in the aquarium, trying to remain alive. The simulation has been implemented in JAVA 3D. A main server and five clients compose the distributed VR version. The server comprehends the simulation of the aquarium and contained fishes, while the clients are responsible for the different views of this aquarium (from inside) projected at each of the five CAVE walls.

Gene duplication theory was first proposed by a Japanese biologist, Ohno, in the 1970s. Inspired by the theory, we developed a gene-duplicating genetic algorithm (GDGA) with several variants. Individuals with various gene lengths are evolved based on a parameter-free genetic algorithm (i.e., a new GA without genetic parameters set in advance) and then genes with different lengths are concatenated by migrating among sub-populations (i.e., a population was divided in advance into several sub-populations according to each gene-duplication type). To verify the algorithm's performance, we previously performed a comparative study and found a relationship between the features of the test functions and the adequate types of gene-duplication. In this study, we further describe how we have extended the scheme by automatically adapting its search strategy to various test functions without a priori knowledge of them, and verify the performance of the adaptive strategy compared to that of an adequate type of gene-duplication.

In this chapter, we will analyse the influence of noise on the search behaviour of evolutionary algorithms. We will introduce different classes of functions which go beyond the simple additive noise model. The first function demonstrates a trade-off between an expectation and a variance based measure for the evaluation of the quality in the context of stochastic optimisation problems. Thereafter, we concentrate on functions whose topology is changed when the expectation value is taken as the quality criterion. In particular, for functions with noise induced multi-modality (FNIM), the process can be regarded as a bifurcation. The behaviour of two types of evolution strategies is analysed for FNIMs.

The performance of genetic algorithms (GAs) is theoretically estimated with multiplicative royal-road functions (mRR-functions). Using a macro-schema analysis, the effects of selection, mutation, and crossover are quantitatively estimated, which enables formulation of the innovation time and takeover time of component schemata as a function of genetic parameters. Theoretical estimation is compared to the experimental results of a simple GA, and it is shown that the theoretical results are in good agreement with experimental ones specifically when the innovation time is much larger than the takeover time.

This chapter presents a real-coded cellular GA model using a new selection method inspired by predator-prey interactions. The model relies on the dynamics generated by spatial predator-prey interactions to maintain an appropriate selection pressure and diversity in the prey population. In this model, prey, which represent potential solutions, move around on a two-dimensional lattice and breed with other prey individuals. The selection pressure is exerted by predators, which also roam around to keep the prey in check by removing the weakest prey in their vicinity. This kind of selection pressure efficiently drives the prey population to greater fitness over successive generations. Our preliminary study has shown that the predator-prey interaction dynamics play an important role in maintaining an appropriate selection pressure in the prey population, thereby helping to generate suitably fit prey solutions. Our experimental results are comparable or better in performance than those of a standard serial and distributed real-coded GA.

The 'bi' and 'higher modal features' are aspects of Evolutionary Algorithm (EA) behaviors that are revealed, for a wide range of conditions, when extensive parametric studies are done to explore convergence time over a wide range of mutation rates. The bimodal feature indicates optimal mutation rates in terms of convergence time, which often correspond to optimal mutation rates in terms of final solution quality. The significance of the bimodal feature lies in parameter setting issues, and it is of interest to see how it varies with parameters and EA designs. Previous work shows that it appears in a wide range of conditions, but attenuates (the local optimum in convergence time becomes less apparent) with larger population sizes and low selection pressure. This chapter extends exploration of the bimodal feature into EAs with much larger population sizes, and shows that under sufficiently high selection pressure it 'returns'. It is interesting to note that these observations apply directly in the emerging field of 'Directed Evolution' for novel bio-molecules, in which large parallel populations undergo evolutionary search, with solution quality and number of generations being vital to optimise. This has potentially highly significant consequences for setting of mutation rates in Directed Evolution and high selection pressure large-scale parallel EAs in general.

In evolutionary algorithms based on probabilistic modeling, the offspring population is generated according to the estimated probability density model of the parent instead of using recombination and mutation operators. In this chapter, we have proposed a probabilistic model-building genetic algorithms (PMBGAs) for solving flow shop scheduling problems using edge histogram based sampling algorithms (EHBSAs). The effectiveness of introducing the tag node (TN) in a string representation is also discussed.

This chapter presents a simulation model of multiple autonomous mobile robots with behavior models of a fish school. Although a school of fish does not need a special individual to lead it, an autonomous movement emerges from interactions among neighboring bodies. This study aims to realize autonomous collective movements of mobile robots through interactions among robots like a fish school. We used Khepera robots to simulate mobile cars running on a freeway. Autonomous behavior is assumed for the robots to run freely by sensing neighboring robots or the guard rails by means of infrared rays. An evaluation function was applied to free style running robots to run forward efficiently without colliding with other robots or guard rails. Genetic algorithms(GA) were applied to optimize the behavior models of a robot. As a result, we have obtained a nice collective movement of multirobots, with high techniques, such as speeding up, slowing down, and overtaking a slow robot ahead.

Decomposing a complex computational problem into sub-problems, which are computationally simpler to solve individually and which can be combined to produce a complete solution, can efficiently lead to compact and general solutions. Neural network ensemble is one such modular system that uses this divide-and-conquer strategy. Diverse set of networks improves ensemble's performance over its constituent networks. Artificial speciation is used here to produce this diverse set of networks that solve different parts of a data classification task and complement each other in solving the complete problem. Fitness sharing is used in evolving the group of neural networks to achieve the required speciation. Sharing is performed at phenotypic level using modified Kullback-Leibler entropy as the distance measure. The group as a unit solves the classification problem and outputs of all the networks are used in finding the final output. For the combination of neural network outputs 3 different methods - Voting, averaging and recursive least square are used. The evolved system is tested on two data classification problems (Heart Disease Dataset and Breast Cancer Dataset) taken from UCI machine learning benchmark repository.

Early search engines had their origin in information retrieval systems. These systems were typically developed by human editors to index a document set that was static over long periods of time. The information retrieval systems provided a stable user environment that was eventually optimized over time and facilitative of incremental growth in the document collection. Early search engines used this tried and tested information retrieval model, but encountered usability limitations when the document growth rate accelerated. The limitations of the model became magnified as the need for automated indexing mechanisms grew, and information retrieval systems began to be used with dynamic document datasets. These limitations are still apparent in current search engines which incorporate aspects of these early information retrieval systems. This chapter presents the Tocorime Apicu approach for replacing the information retrieval model with an information sharing model that adapts to changing conditions within the Internet using the stochastic optimization methodologies of evolutionary computation. Experimental results are presented.

With the popularity of evolutionary multi-objective optimization (EMO) methods among researchers and practitioners, an increasing interest has grown in developing new and computationally efficient algorithms and in comparing them with existing methods. Unlike in single-objective optimization in which often the goal is to find a single optimal solution, an EMO method attempts to find a set of well-converged and well-distributed set of trade-off solutions. In comparing two or more EMO methods, it is intuitive that more than one performance metrics are necessary. Although there exist a number of performance metrics in the EMO literature, they are usually applied to the final non-dominated set obtained by an EMO algorithm to evaluate its performance. In this chapter, we emphasize the need of running performance metrics, which will provide the dynamics of the working of an EMO algorithm. Either using a known Pareto-optimal front or an agglomeration of generation-wise populations, two suggested metrics reveal important insights and interesting dynamics of the working of an EMO and help provide a comparative evaluation of two or more EMO methods.

One advantage of evolutionary computation over conventional optimization and search techniques is its ability to deal with multiple objectives. Multi-objective evolutionary algorithms (MOEAs) have proved very useful in many real-world applications and the number of publications in this area has exceeded 1000. However, no one offers a simple-to-use or widely accepted method for evaluating the performance of MOEAs. This is largely due to difficulties in visualizing non-dominated solutions in a multi-objective space when the number of objectives exceeds three. In this chapter, we propose a new visualization technique that should provide better understanding of high order multi-dimensional objectives, so as to assist the design and refinement of MOEAs.

In this chapter, a new unsupervised image clustering approach which is based on the particle swarm optimization (PSO) algorithm is presented. The algorithm finds the centroids of a user specified number of clusters, where each cluster groups together similar pixels. The new image clustering algorithm has been applied successfully to three types of images to illustrate its wide applicability. These images include synthetic, MRI and Satellite images. A comparison between the new approach and the well-known K-means clustering algorithm is provided to show the efficiency of PSO in the area of image clustering.

This chapter is devoted to an application of genetic algorithms and coevolutionary principles to a large optimization problem. Starting point is a mixed integer linear program which models our problem—in this case a facility layout problem. As the number of binary variables increases quadratically with the problem size, currently available solvers fail already for small problem instances. Using a genetic search our algorithm reduces the number of binary variables by setting a considerable part of them. The genetic operators were specially designed to yield a high percentage of feasible variable settings. In order to further speed up the computation of large problems we propose a partition into interdependent subproblems. Each subproblem ("species") is evolved by a genetic algorithm respecting the constraints ("environment") generated by the others. Numerical experiments verify this revolutionary approach.

In real world engineering design problems we have to search for solutions that simultaneously optimize a wide range of different criteria. Furthermore, the optimal solutions also have to be robust. Therefore, this chapter describes a method where a multi-objective genetic algorithm is combined with response surface methods in order to assess the robustness of a set of identified optimal solutions. The multi-objective genetic algorithm is used in order to optimize two different concepts of hydraulic actuation systems. The different concepts have been modeled in a simulation environment to which the optimization strategy has been coupled. The outcome from the optimization is a set of Pareto optimal solutions that elucidate the tradeoff between the energy consumption and the control error for each actuation system. Based on these Pareto fronts, promising regions could be identified for each concept. In these regions sensitivity analyses are performed with the help of response surface methods. It can then be determined how different design parameters affect the system for different optimal solutions.

In this chapter, an integrated production and transportation scheduling model is proposed. This model is based on multi-item capacitated lot sizing and facility location type models. The objective of the integrated model is to minimize the total production and transportation cost. The integrated model is solved by Lagrangian decomposition method and the decomposed two sub-problems can be solved by genetic algorithm and Simplex method respectively. Computational results showed that the overall cost is reduced by 4% to 10% compared with the other two sequential optimization algorithms.

Fuzzy logic controllers have been applied to a wide range of control problems, but are very difficult to build for situations where the environment changes quickly and there is a lot of uncertainty. This work investigates a new method of creating fuzzy controllers, in the form of reactive agents, for such environments. The framework for this investigation is the RoboCup soccer simulation environment, where the agents are in the form of simulated soccer players evolved to exhibit competent dribble-and-score behaviours. The method proposed uses a messy genetic algorithm to evolve a set of behaviour producing fuzzy rules which define the agents. The results presented indicate that the messy genetic algorithm is well suited to this task, producing good performance by reducing complexity, and that the agents produced perform well in their environment. The best agent evolved is consistently and reliably able to locate the ball, dribble it to the goal and score.

We present single-rail and dual-rail mixed pass-transistor logic (PTL) synthesis method based on genetic search and compared the results with their conventional static CMOS counterparts synthesized using a commercial logic synthesis tool in terms of area, delay and power in an experimental 0.1µm and 0.13µm CMOS technologies as well as a 0.13µm floating-body partially depleted silicon-on-insulator (PDSOI) process. Our experimental results demonstrate that both single-rail and dual-rail mixed PTL circuits synthesized using the proposed mixed PTL/CMOS synthesis method outperforms their static counterparts in delay and power in bulk CMOS as well as SOI CMOS technologies.

This chapter investigates the use of a multi-objective approach for evolving artificial neural networks that act as controllers for the legged locomotion of a 3-dimensional, artificial quadruped creature simulated in a physics-based environment. The Pareto-frontier Differential Evolution (PDE) algorithm is used to generate a Pareto optimal set of artificial neural networks that optimizes the conflicting objectives of maximizing locomotion behavior and minimizing neural network complexity. The evolutionary and operational dynamics of controller evolution is analyzed to provide an insight into how the best controller emerges from the artificial evolution and how it generates the emergent walking behavior in the creature. A comparison between Pareto optimal controllers showed that artificial neural networks (ANNs) with varying numbers of hidden units resulted in noticeably different locomotion behaviors. We also found that a much higher level of sensory-motor coordination was present in the best evolved controller. Finally we investigated the effects of environmental, morphological and nervous system changes on the artificial creature's behavior and found that certain changes are detrimental to the creature's locomotion capability.

This chapter presents an application of Bayesian network technology in an empirical customer satisfaction study. The findings of the study should provide insight to the importance of product/service dimensions in terms of the strength of their influence on overall (dis)satisfaction. To this end we apply a sensitivity analysis of the model's probabilistic parameters, which enables us to classify the dimensions with respect to their (non) linear and synergy effects on low and high overall satisfaction judgments. Selected results from a real-world case study are shown to demonstrate the usefulness of the approach.

Hyper-GA was introduced by the authors as a genetic algorithm based hyper-heuristic which aims to evolve an ordering of low-level heuristics so as to find a good quality solution for a given problem. The adaptive length chromosome hyper-GA (ALChyper-GA) is an extension of our previous work, in which the chromosome was of fixed length. The aim of a variable length chromosome is two fold; 1) it allows dynamic removal and insertion of heuristics 2) it allows the GA to find a good chromosome length which could otherwise only be found by experimentation. We apply the ALChyper-GA to a trainer scheduling problem and report that good quality solutions can be found. We also present results for four versions of the ALChyper-GA, applied to five test data sets.

In this chapter, design of a 100 kW and 100,000 rpm surface mounted type Permanent Magnet synchronous machine has been optimized using Genetic Algorithms. The efficiency and power density of the machine have been taken as objective functions. A third objective function has been formed using weightage ratio method, giving equal weightage to both efficiency and power density. Various electrical, mechanical and thermal constraints have been taken into account using conventional formulae. Variation of efficiency and specific power with speed (at fixed output power) and with output power (at fixed speed) has been discussed. The results have been compared with that obtained using Sequential Unconstrained Minimization Technique (SUMT) based on the interior penalty function method.

This chapter presents the use of multi-objective Genetic Algorithms (mGA) to solve the capacity and routing assignment problem arising in the design of self-healing networks using the Virtual Path (VP) concept. Past research has revealed that Pre-planned Backup Protection method and the Path Restoration scheme can provide a good compromise on the reserved spare capacity and the failure restoration time. The aims to minimize the sum of working and backup capacity usage and transmission delay often compete and contradict with each other. Multi-objective Genetic algorithm is a powerful method for this kind of multi-objective problems. In this chapter, a multi-objective GA approach is proposed to achieve the above two objectives while a set of customer traffic demands can still be satisfied and the traffic is 100% restorable under a single point of failure. We carried out a few experiments and the results illustrate the trade-off between objectives and the ability of this approach to produce many good compromise solutions in a single run. To measure the performance of approach, our results are used to compare with that using single objective genetic algorithm (sGA).

In this chapter, we propose to apply Genetic Algorithm to Gold codes, Kasami codes and Multiple Spreading to generate sets of CDMA sequences. The analysis has shown us that the GA based pseudo noise (PN) codes have better Signal to noise ratio (SNR) and lower Bit error rate (BER). It also indicated that the Multiple Spreading has many advantages over the other PN code, however, its set of sequences still is not the best. By using Genetic Algorithm, a set of sequences with better SNR and BER can be achieved. The reason is that Multiple Spreading can provide some good genes for optimization. Therefore, we can conclude that the GA based Multiple Spreading is a good solution to generate PN codes for DS-CDMA system.

The multi-constrained least-cost multicast routing is a challenging problem in multimedia networks. Computing such a constrained Steiner tree is an NP-complete problem. We propose a novel solution to this problem based on genetic algorithms (GA). The proposed solution consists of several new heuristic algorithms for mutation, crossover, and creation of random individuals. The predecessors encoding scheme is used for genotype representation. We evaluate the performance and efficiency of the proposed GA-based algorithm in comparison with other existing heuristic and GA-based algorithms using simulation results. The most efficient combination of various proposed alternative algorithms is selected as our final solution based on the simulation results. This proposed GA-based algorithm has overcome the existing algorithms considering average tree cost and running time.

Optimization based on genetic algorithms is applied to the design of multilayered coatings, incorporating both coating-geometry and material-property optimization. The latter is based on parametric modeling of dielectric and magnetic properties of homogeneous materials, and effective medium modeling of composites. Our approach treats physical laws to be obeyed by the models as constraints. Moreover, efficiency in thickness is considered in two ways: as an upper-limit constraint, and in multiobjective settings including aggregation and Pareto optimality.

Exploration of real data sets is a complex task that often involves tiresome, manual parameter tuning. Such manual operation, aimed at transformations of data that enable discovery of interesting patterns, only rarely guarantees any thorough examination of all promising combinations of parameter values. To avoid this inconvenience, we present a universal data transformation approach that has the ability to conduct fully automatic adjustments of parameter values. The main mechanism is based on a genetic algorithm designed to search for parameter settings that are optimal with respect to a pre-defined objective function. As an illustration of the procedure we present a system that improves classification of vowels by constructive induction of new features (attributes). The new features are created in a process that is entirely automatic: the original data are transformed with a set of sequentially applied operators, the parameters of which are incorporated in a genome and thus easily controlled by the genetic search engine. The results of several conducted experiments prove the usefulness of the proposed approach.

The loss of refrigerant gas from commercial refrigeration systems is a major maintenance cost for most supermarket chains. Gas leaks can also have a detrimental effect on the environment. Existing monitoring systems maintain a constant watch for faults such as this, but often fail to detect them until major damage has been caused. This chapter describes a system which uses real-world data received at a central alarm monitoring centre to predict the occurrence of gas leaks. Evolutionary algorithms are used to breed neural networks which achieve usefully high accuracies given limited training data.

We explore a novel application of Genetic Algorithms, viz., as an empirical method in two sub-areas of the Analysis of Algorithms. First, Approximation Algorithms provide tractable solutions to NP-Complete problems while sacrificing solution quality. Second, Online Algorithms are designed for the case in which the problem instance does not arrive in its totality, as in Offline Algorithms, but arrives piece by piece, over the course of the computation. Generating worst-case instances for either algorithm type, for use both as test cases and in lower-bound proofs, is often non-trivial. We use GAs to find worst-case instances of several NP-Complete problems, including the Traveling Salesman Problem, and of Online problems, including versions of the Taxicab Problem. These worst-case instances give us lower bounds on the competitiveness of the approximation algorithms used. For example, they provide empirical results suggesting that the greedy algorithm, in the worst case, does better on planar graphs than on arbitrary graphs. In addition, they demonstrate that 6.93 is a lower bound on the competitiveness of the "hedging" algorithm for the Hard Planar Taxicab Problem. This experimental result has theoretical implications for the study of the problem, i.e., that further research to prove an upper bound of 7 may be warranted.

Prediction of protein secondary structure is considered as an important medium step towards determining its three-dimensional structure and function. We have developed Multi-modal Neural Networks (MNNs) to improve the accuracy of the prediction. The MNN employs several sub-networks to predict the secondary structure individually and produce the final result from the outputs of the sub-networks by the majority decision. Moreover, we expand the MNN into a twofold MNN to enhance the prediction ability.

Mimesis should be meaningful imitation that reproduces not only superficial behaviors but also the behavioral intentions underlying the behaviors. We propose mimetic learning based on a self-evaluation function (MLSE), which enables the imitator robot to assess the model robot's behavioral intention by referring to the rate of change of its self-evaluation function. Here, the success of the imitation depends on a coevolving mechanism that consists of two learning contexts: identical and situational contexts. Experimental results show that MLSE enables the imitator robot to reproduce behavioral patterns by taking into account the model robot's behavioral intention. Additionally, the imitator robot succeeded in reproducing the model robot's intention even if there was a slight difference between the model and the imitator robot's body size. In this chapter, this phenomenon is regarded as a special case of joint attention in the mimetic context. As a result, this chapter aims to answer the question "What is a mimetic same?"

In this chapter, we propose a multi-agent system aiming at autonomous symbol acquisition, in which agents acquire symbols through communication instead of symbols being given a priori by the designer. Based on this idea, we extended Steels's language acquisition model to develop a new model featuring three mechanisms: (a) symbol matching; (b) symbol creation; and (c) concept selection. Intensive simulation revealed the following implications: (1) the degree of trade-off between communication success and required lexicon size can be decreased by matching all possible combinations in symbol matching; (2) symbol creation of hearer agents plays a significant role in symbol acquisition, while speaker agents do not; (3) the speed of symbol creation depends on the method used for this step, but it is not related to the trade-off between communication success and lexicon size; and (4) concept selection can also be applied to resolve the trade-off between communication success and required lexicon size.

This chapter discusses the searching capability of genetic algorithms for vision-based mobile robots from the viewpoints of ecological psychology. The perceptual system of a mobile robot is restricted by the action system in the spatio-temporal context of the facing environment. In this chapter, we propose a time-series of a searching method in the visual perception according to the current situation and action outputs. Furthermore, we show several experimental results of two different mobile robots.

This chapter investigates into recursive neural networks and their application in time series forecast. As one of the most popular recurrent neural networks, an Elman neural network is studied in this chapter. It has been proven that the Elman network is able to approximate the trajectory of a given dynamic system for any fixed length of time. This ability is explored in the area of time series forecasting. The electricity market demand signal, as a typical time series, is studied in the chapter with Elman networks. In order to obtain the best available optimal weight allocation, a Genetic Algorithm (GA) is used to train the recurrent neural networks in the forecast model. The forecast simulation is carried out on electricity market load data series with Elman networks as well as GA trained Elman networks to compare their performance.

Shared control is a challenge to combine and capitalize on both advantages of human and mechanized automatic controls. But any interventions by a machine-autonomy may introduce some kind of collapses in the isomorphism between the two different interaction fields of the human operation and of the system performance, which is necessary for the naturalistic involvement of a human operator with the system control. On this issue, we focus on the adaptability for the machine-autonomy to coordinate the mapping between the two fields to facilitate the human feeling of direct involvement with the system control, as well as the human adaptation (i.e., co-adaptation). In this chapter, we investigate their joint activity in a shared control of a virtual robot teleoperation environment, and then discuss the effect of the machine intervention in terms of the system operationality for human operators. The feasibility of the co-adaptive approach towards the well-coordinated relationship between a manipulator and a manipulatee is also examined based upon the experimental result with a simple adaptation algorithm implemented into the machine-autonomy.

DNA microarray has been heavily utilized in analysis of gene expression and has made tremendous impact in many disciplines of life sciences. A most standard microarray technology in a typical laboratory setting is printing DNA molecules onto glass slides using robots. In order to distinguish nucleic acids with very similar composition by hybridization, it is necessary to design probes with high specificities, i.e. uniqueness. We make use of the available sequence information of all the yeast open reading frames (ORF) combined with an distributed evolutionary computational (DEC) strategy to search for unique sequences to represent each and every ORF in the yeast genome. The results are presented and discussed. The capability of the DEC is demonstrated to be efficient and robust. The approach can be extended to more complicated genomes.

Educational institutions are usually involved in planning timetables for examination and curriculum. Of the two, the latter is usually more complex. It is typical that institutions are usually faced with a set of unique requirements. Each curriculum timetable consists of several components and they tend to differ significantly among institutions. The curriculum timetable of the school of Electrical and Electronic Engineering (EEE) in Nanyang Technological University (NTU), which has five components, is no exception. Each of the components can be planned separately as an individual schedule. Each of these schedules constitutes a species and co-evolved together to obtain the optimum timetable. Multi-schedule evolution has been found to be effective in finding a feasible optimum curriculum timetable in the case of EEE, NTU.