Narrow Results

The particle swarm optimization (PSO) algorithm and two variants of the evolutionary programming (EP) are applied to the several function optimization problems and the conformation optimization of atomic clusters to check the performance of these algorithms as a general-purpose optimizer. It was found that the PSO is superior to the EP though the PSO is not equipped with the mechanism of self-adaptation of search strategies of the EP. The PSO cannot find the global minimum for the atomic cluster but can find it for similar multi-modal benchmark functions of the same size. The size of the cluster which can be handled by the PSO and the EP is limited, and is similar to the one amenable to the popular simulated annealing. The result for benchmark functions only serves as an indication of the performance of the algorithm.

Self-adaptation has been frequently employed in evolutionary computation. Angeline¹ defined three distinct adaptive levels which are: population, individual and component levels. Cultural Algorithms have been shown to provide a framework in which to model self-adaptation at each of these levels. Here, we examine the role that different forms of knowledge can play in the self-adaptation process at the population level for evolution-based function optimizers. In particular, we compare the relative performance of normative and situational knowledge in guiding the search process. An acceptance function using a fuzzy inference engine is employed to select acceptable individuals for forming the generalized knowledge in the belief space. Evolutionary programming is used to implement the population space. The results suggest that the use of a cultural framework can produce substantial performance improvements in execution time and accuracy for a given set of function minimization problems over population-only evolutionary systems.

Cultural Algorithms are computational self-adaptive models which consist of a population and a belief space. The problem-solving experience of individuals selected from the population space by the acceptance function is generalized and stored in the belief space. This knowledge can then control the evolution of the population component by means of the influence function. Here, we examine the role that different forms of knowledge can play in the self-adaptation process within cultural systems. In particular, we compare various approaches that use normative and situational knowledge in different ways to guide the function optimization process.

The results in this study demonstrate that Cultural Algorithms are a naturally useful framework for self-adaptation and that the use of a cultural framework to support self-adaptation in Evolutionary Programming can produce substantial performance improvements over population-only systems as expressed in terms of (1) systems success ratio, (2) execution CPU time, and (3) convergence (mean best solution) for a given set of 34 function minimization problems. The nature of these improvements and the type of knowledge that is most effective in producing them depend on the problem's functional landscape. In addition, it was found that the same held true for the population-only self-adaptive EP systems. Each level of self-adaptation (component, individual, and population) outperformed the others for problems with particular landscape features.

In this paper a family of new methods are proposed for constructing hierarchical-interpolative fuzzy rule bases in the frame of a fuzzy rule based supervised machine learning system modeling black box systems defined by input-output pairs. The resulting hierarchical rule base is constructed by using structure building pure evolutionary and memetic techniques, namely, Genetic and Bacterial Programming Algorithms and their memetic variants containing local search steps.

Applying hierarchical-interpolative fuzzy rule bases is a rather efficient way of reducing the complexity of knowledge bases, whereas evolutionary methods (including memetic techniques) ensure a relatively fast convergence in the learning process.

As it is presented in the paper, by applying a newly proposed representation schema these approaches can be combined to form hierarchical-interpolative machine learning systems.

The paper presents two alternative schemes for pricing European and American call options, both based on artificial neural networks. The first method uses binomial trees linked to an innovative stochastic volatility model. The volatility model is based on wavelets and artificial neural networks. Wavelets provide a convenient signal/noise decomposition of the volatility in the non-linear feature space. Neural networks are used to infer future volatility levels from the wavelets feature space in an iterative manner. The bootstrap method provides the 95% confidence intervals for the options prices. In the second approach neural networks are trained with genetic algorithms in order to reverse-engineer the Black–Scholes formulae. The standard Black–Scholes model provides a starting point for an evolutionary training process, which yields improved options prices. Market options prices as quoted on the Chicago Board Options Exchange are used for performance comparison between the Black–Scholes model and the proposed options pricing schemes. The proposed models produce as good as and often better options prices than the conventional Black–Scholes formulae.

An algorithm is presented for projecting — at the amino acid level — the three-dimensional crystal structure of a protein molecule onto a planar surface. The scheme is topologically consistent: if two amino acid residues are closely juxtaposed in three-dimensional space, they remain so upon projection. Through such projections, a single resulting picture captures the spatial relations amongst a protein molecule's amino acids. Operationally, a genetic algorithm is used to "evolve" a parameter set which serves as input for a self-organizing Kohonen neural network responsible for the projection itself. A fitness function characterizing the quality of the projections is defined and maximized via the genetic algorithm. The workings of both the genetic algorithm and neural network are discussed in detail. In this work, we seek to optimize projections resulting from the inherently "frustrated" task of collapsing a space-filling collection of amino acid residues onto a simpler surface. Ultimately, the chosen application is a testing ground for establishing the success of our coupled genetic algorithm/Kohonen neural network scheme which can easily be adapted for other uses.

This paper presents an efficient, simple and new method for solving Economic Dispatch (ED) problem with line flow constraints through the application of Evolutionary Programming (EP). The controllable system quantities in the base-case state are optimized to minimize some defined objective function subject to the base-case operating constraints. A 10-bus system is taken for investigation. The ED results obtained using EP are compared with those obtained using quadratic programming. The investigations reveal that the proposed algorithm is relatively simple, reliable, efficient and suitable for on-line applications.

We present a new approach to acquisition of comprehensible fuzzy rules for fuzzy modeling from data using Evolutionary Programming (EP). For accuracy of model, it is effective to allow overlapping of membership functions with each other in the fuzzy model. From the viewpoint of knowledge acquisition, it is desirable that the model has a smaller number of membership functions with less overlapping. Considering the trade-off between the precision and the clarity of the fuzzy model, this paper presents an acquisition method of comprehensible fuzzy rules form the identified model that satisfies the desired accuracy. The approach clearly distinguishes modeling phase and re-evaluation phase. The accurate model of unknown system in the modeling phase is to be obtained by, for example, fuzzy neural network (FNN) such as a radial basis function network, using EP. The simplified model in the re-evaluation phase can mainly be used for knowledge acquisition from unknown system. A numerical experiment was done to show the feasibility of the proposed algorithm.