Narrow Results

Inspired by the work of Ray et al., we study a model of predator-prey dynamics that incorporates the effects of a discrete genotype. We thoroughly analyze the many features of the model, and show that the system seems to reach a critical state in the genotype space, with some evidence of self-organization. Our results present the effects of natural selection at work in genotype space. The presence of the discrete genotype seems to make the model more robust to small variations of the main parameters, when compared to the bare Lotka–Volterra dynamics.

In a large common place, a huge number of pedestrians may flood into the surrounding region and mix with the vehicles which originally existed on the roads when emergent events occur. The mutual restriction between pedestrians and vehicles as well as the mutual effect between evacuation individuals and the environment which evacuees are situated in, will have an important impact on evacuation effects. This paper presents a pedestrian–vehicle mixed evacuation model to produce optimal evacuation plans considering both evacuation time and density degree. A co-evolutionary multi-particle swarms optimization approach is proposed to simulate the evacuation process of pedestrians and vehicles separately and the interaction between these two kinds of traffic modes. The proposed model and algorithm are effective for mixed evacuation problems. An illustrating example of a study region around a large stadium has been presented. The experimental results indicate the effective performances for evacuation problems which involve complex environments and various types of traffic modes.

The co-evolution of species with their genomic parasites (transposons) is thought to be one of the primary ways of rewiring gene regulatory networks (GRNs). We develop a framework for conducting evolutionary computations (EC) using the transposon mechanism. We find that the selective pressure of transposons can speed evolutionary searches for solutions and lead to outgrowth of GRNs (through co-option of new genes to acquire insensitivity to the attacking transposons). We test the approach by finding GRNs which can solve a fundamental problem in developmental biology: how GRNs in early embryo development can robustly read maternal signaling gradients, despite continued attacks on the genome by transposons. We observed co-evolutionary oscillations in the abundance of particular GRNs and their transposons, reminiscent of predator-prey or hostparasite dynamics.

As the traditional gray forecasting model GM(1, 1) has poor performance in forecasting the fast-growing power load, we present a chaotic co-evolutionary particle swarm optimization (CCPSO) algorithm, one with better efficiency than the PSO algorithm. Based on the GM(1, 1) model, the CCPSO algorithm is adopted to solve the values of parameters a and b in GM(1, 1) model. This is how the way we come up with the CCPSO algorithm-based GM. As can be seen from experimental results of case simulation on the power consumption in three regions, the CCPSO-GM model is better than the other four forecasting models, which attests its wide applicability and high forecasting accuracy.

This paper examines the interplay of opinion exchange dynamics and communication network formation. An opinion formation procedure is introduced which is based on an abstract representation of opinions as k-dimensional bit-strings. Individuals interact if the difference in the opinion strings is below a defined similarity threshold d_I. Depending on d_I, different behavior of the population is observed: low values result in a state of highly fragmented opinions and higher values yield consensus. The first contribution of this research is to identify the values of parameters d_I and k, such that the transition between fragmented opinions and homogeneity takes place. Then, we look at this transition from two perspectives: first by studying the group size distribution and second by analyzing the communication network that is formed by the interactions that take place during the simulation. The emerging networks are classified by statistical means and we find that nontrivial social structures emerge from simple rules for individual communication. Generating networks allows to compare model outcomes with real-world communication patterns.

Riboswitch can bind small molecules to regulate gene expression. Unlike other RNAs, riboswitch relies on its conformational switching for regulation. However, the understanding of the switching mechanism is still limited. Here, we focussed on the add A-riboswitch to illustrate the dynamical switching mechanism as an example. We performed molecular dynamics simulation, conservation and co-evolution calculations to infer the dynamical motions and evolutionary base pairings. The results suggest that the binding domain is stable for molecule recognition and binding, whereas the switching base pairings are co-evolutionary for translation. The understanding of the add A-riboswitch switching mechanism provides a potential solution for riboswitch drug design.

Small firms facing today’s turbulent business environment often fail early in their life if they do not develop the necessary capabilities to survive. The main goal of this study is to investigate how IT and knowledge co-evolve, influencing a firm’s agility, within the context of micro and small enterprises (MSEs). Applying the resource-based view of the firm and dynamic capabilities, a multiple case study of eight firms was used to explore links among business, IT and knowledge strategies, resources, and capabilities. Links among IT and knowledge capabilities and firm agility were also explored. The results demonstrate that an MSE’s business strategy shapes, and is also shaped by, the firm’s IT and knowledge strategies; and that both IT and knowledge capabilities shape, and are shaped by, the firm’s agility, coevolving with it. By highlighting the important antecedents of small firm agility and presenting crucial links among agility, IT capabilities, and knowledge capabilities in MSEs, we encourage practitioners to think carefully about their IT and knowledge strategies and to rethink their use of firm resources and capabilities to develop agility in the face of environmental uncertainty and change.

Sequence-specific and consequential interactions within or between proteins and/or RNAs can be predicted by identifying co-evolution of residues in these molecules. Different algorithms have been used to detect co-evolution, often using biological data to benchmark a methods ability to discriminate against indirect co-evolution. Such a benchmark is problematic, because not all the interactions and evolutionary constraints underlying real data can be known a priori. Instead, sequences generated in silico to simulate co-evolution would be preferable, and can be obtained using aCES, the software tool presented here. Conservation and co-evolution constraints can be specified for any residue across a number of molecules, allowing the user to capture a complex, realistic set of interactions. Resulting alignments were used to benchmark several co-evolution detection tools for their ability to separate signal from background as well as discriminating direct from indirect signals. This approach can aid in refinement of these algorithms. In addition, systematic tuning of these constraints sheds new light on how they drive co-evolution between residues. Better understanding how to detect co-evolution and the residue interactions they predict can lead to a wide range of insights important for synthetic biologists interested in engineering new, orthogonal interactions between two macromolecules.

The external triggers cause transitions; to stay competitive, organizations create, respond to change, and shape the environment they interact with. The interactions manifest their traits as characteristics. Like biological species, organizations possess memes (or genotypes), memes mature as ideas or innovations, their interactions with the environment manifest as characteristics (phenotypes). IT organizations experience fast changes, for better response to change adopted agile software development (ASD). When phenotypes involve the development of affordances and display plasticity (agility), apart from cost-benefit criteria, it contributes to co-evolution among organizations and the environment. To examine the phenomena of co-evolution with diverse analytic and heuristic views, the author used multiple ASD case-studies from IT product$/$services organizations. The author noted that organizations increase the height of their fitness landscape, i.e., competitive advantage, by either developing and$/$or acquiring phenotypes. Acquisition meets cost-benefit criteria, but does not assist in sustaining the height, co-evolution, and$/$or cumulate as stable designs, thereby, there are inappropriate responses to external triggers.

In this paper, a novel genetic algorithm based approach is proposed for optimal sensor placement and controller design of a mobile robot to facilitate its reactive navigation and obstacle avoidance in unknown environments. The mobile robots considered in this paper have flexible sensor and control structure. A genetic algorithm is developed to evolve the parameters of optimal sensor placement and controller design simultaneously. The effectiveness of the proposed GA based co-evolution approach to robot sensor placement and control design is demonstrated by simulation studies.

In this work a co-evolutionary approach is used in conjunction with Genetic Programming operators in order to find certain transition rules for two-step discrete dynamical systems. This issue is similar to the well-known artificial-ant problem. We seek the dynamic system to produce a trajectory leading from given initial values to a maximum of a given spatial functional.

This problem is recast into the framework of input-output relations for controllers, and the optimization is performed on program trees describing input filters and finite state machines incorporated by these controllers simultaneously. In the context of Genetic Programming there is always a set of test cases which has to be maintained for the evaluation of program trees. These test cases are subject to evolution here, too, so we employ a so-called host-parasitoid model in order to evolve optimizing dynamical systems.

Reinterpreting these systems as algorithms for finding the maximum of a functional under constraints, we have derived a paradigm for the automatic generation of adapted optimization algorithms via optimal control. We provide numerical examples generated by the GP-system MathEvEco. These examples refer to key properties of the resulting strategies and they include statistical evidence showing that for this problem of system identification the co-evolutionary approach is superior to standard Genetic Programming.

Perhaps the best known example of user-guided evolution is furnished by evolving expressions, an image generation technique first introduced by Sims. In this version of artificial evolution, images are evolved for aesthetic purposes, hence any fitness measure used must be based on aesthetics. We consider the problem of guiding image evolution autonomously on the basis of computational, as opposed to user-assigned, aesthetic fitness. Due to the difficulty of formulating an absolute criterion for aesthetic fitness, we adopt a coevolutionary approach, relying on hosts and parasites to establish relative criteria for aesthetic fitness. To sustain the coevolutionary arms race, we allow coevolution to proceed in stages. This permits appropriate fitness levels to be maintained within the parasite populations we use to infect host image populations. Using staged coevolution produces two beneficial results: (1) longer survival times for subpopulations of host images, and (2) stable phenotypic lineages for host images.

In this paper, we will propose a novel framework of hybridization of Coevolutionary Genetic Algorithm and Machine Learning. The Coevolutionary Genetic Algorithm (CGA) which has already been proposed by Handa et al. consists of two GA populations: the first GA (H-GA) population searches for the solutions in given problems, and the second GA (P-GA) population searches for effective schemata of the H-GA. The CGA adopts the notion of commensalism, a kind of co-evolution. The new hybrid framework incorporates a schema extraction mechanism by Machine Learning techniques into the CGA. Considerable improvement in its search ability is obtained by extracting more efficient and useful schemata from the H-GA population and then by incorporating those extracted schemata into the P-GA. We will examine and compare two kinds of machine learning techniques in extracting schema information: C4.5 and CN2. Several computational simulations on multidimensional knapsack problems, constraint satisfaction problems and function optimization problems will reveal the effectiveness of the proposed methods.

There is a widespread perception that in conflict situations, more intermediate choices between full peace and total war makes full peace less likely. This view is a motivation for opposing the proposed National Missile Defense. This perception is partly due to research in the abstract game of Iterated Prisoner's Dilemma. This paper critically evaluates this perception.

Mechanisms of promoting the evolution of cooperation in two-player, two-strategy evolutionary games have been discussed in great detail over the past decades. Understanding the effects of repeated interactions in n-player with n-choice is a formidable challenge. This paper presents and investigates the application of co-evolutionary training techniques based on particle swarm optimization (PSO) to evolve cooperation for the iterated prisoner's dilemma (IPD) game with multiple choices. Several issues will be addressed, which include the evolution of cooperation and the evolutionary stability in the presence of multiple choices and noise. First is using PSO approach to evolve cooperation. The second is the consideration of real-dilemma between social cohesion and individual profit. Experimental results show that the PSO approach evolves the cooperation. Agents with stronger social cognition choose higher levels of cooperation. Finally the impact of noise on the evolution of cooperation is examined. Experiments show the noise has a negative impact on the evolution of cooperation.

In this paper, co-evolution is used to examine the long-term evolution of business models in an industry. Two types of co-evolution are used: synchronous, whereby the entire population of business models is replaced with a new population at each generation, and asynchronous, whereby only one individual is replaced.

The alignment issue is an important concern in Systems and Information Systems (IS) Engineering. System alignment aims at ensuring systems are consistent with different items such as business processes, legislation, and organization strategy. As mentioned by researchers, some alignment issues still remain unsolved. In fact, some problems and difficulties occur when dealing with alignment (conceptual mismatch between items to align, different granularity levels of items, important number of links, etc.). We believe that an intentional approach is needed to deal with these difficulties. We thus develop the IS engineering method, called INSTAL, presented here for the alignment of IS with organization's strategy based on intentional alignment models.

Technology and institutions are important driving forces for industrial development, but the relationship between them has not yet reached a consensus due to different economic theories. On the basis of the evolutionary theory, this paper aims to study the roles co-evolution of technology and institutions played in the development of emerging industry. Taking electric vehicles in China as a case study and the five-year plans for the nodes of industrial development, this paper analyzes the co-evolutionary process of technology and institutions at different stages of industrial development, and concludes that it was institutions that promoted technology innovation during the industrial incubation and infancy periods, while during the growth period, it was technology that drove institutions’ innovation. In order to promote the development of electric vehicle industry, it is necessary to further strengthen institutional innovation for technological and industrial development.

In this chapter, basic life-intelligence, and the evolution and co-evolution dynamics of eco-systems are further examined and compared to certain processes in human organizations. A special reference on human thinking systems as intelligence/consciousness sources, information decoders, information processors and complex adaptive systems (CAS) is re-emphasized. In addition, the significance of connectivity, communications, engagement, and orgmindfulness is analyzed with respect to the Abilene paradox, defensive routines and dialogue, as well as the human agent-agent/system micro-structure and micro-dynamic. The individual local self-centric (local self-enrichment processes) and the global orgcentric (global forces) evolutionary dynamics of intelligent human organizations (no global optimality) and their interacting agents (no optimal rationality) are investigated more explicitly with the exploitation of the certain complexity properties. It is observed that local order (stability of agents and networks/subsystem) is highly critical in human dynamic. It is beneficial to recognize that the intelligent complex adaptive dynamic (iCAD) driving an intelligent human organization (iCAS) is not similar to complex adaptive dynamic (CAD) in totality. This recognition provides a significant foundation and better understanding of the intelligent human organizational micro-structure and dynamic.

Essentially, there is a vital need for the transformed mindset, thinking, values, and expectations of human agents (leaders, actors, and non-actors) to be better synchronized. The intelligent person model (an ideal set of attributes) is introduced to substantiate the criticality of new vital characteristics of the human interacting agents in intelligent human organizations. Primarily, intelligent persons (a new category of agents, in particular, intelligence leaders and synergists) are concurrently intelligence/consciousness-centric, complexity-centric and network-centric. The new set of attributes includes high self-powered, intrinsic leadership, information decoding, smarter evolver, emergent strategist, and futurist capabilities. For instance, such a person is in a better position to function as a smarter evolver and emergent strategist that helps to bind a group (network, community, corporation, nation) of human thinking systems more optimally by elevating the quality of collective intelligence in the organization through better mindfulness, orgmindfulness, symbiosis, self-transcending constructions, co-evolution; deeper recognition of the characteristics of the rugged landscape and red queen race; innovative exploitation of relativistic complexity, and possessing futuristic thinking. Apparently, the presence of intelligent persons/agents will lead to a redefinition in leadership and governance strategy.

In this chapter, the biotic/biological structure of an intelligent human organization is more deeply analyzed. Some fundamental characteristics of highly intelligent biological organisms and other complex adaptive systems are scrutinized and compared with human organizations as composite complex adaptive systems — organizational consciousness, orgmindfulness, collective intelligence, connectivity, engagement. It is crucial for intelligent human organizations to possess these biological and complexity associated characteristics. Structurally, a highly intelligent human organization should resemble a highly intelligent biological being. In particular, the characteristics and significance of consciousness, awareness, self-awareness, mindfulness, orgmindfulness, collective intelligence and quality connectivity are further examined with respect to nurturing the orgmind, intangible structure and physical structure in highly intelligent complex adaptive systems (iCAS).