Coevolutionary GA with schema extraction by machine learning techniques and its application to knapsack problems

The authors introduce a novel coevolutionary genetic algorithm with schema extraction by machine learning techniques. Our CGA consists of two GA populations: the first GA (H-GA) searches for the solutions in the given problems and the second GA (P-GA) searches for effective schemata of the H-GA. We aim to improve the search ability of our CGA by extracting more efficiently useful schemata from the H-GA population, and then incorporating those extracted schemata in a natural manner into the P-GA. Several computational simulations on multidimensional knapsack problems confirm the effectiveness of the proposed method</p

Perception-action rule acquisition by coevolutionary fuzzy classifier system

Recently, many researchers have studied the techniques in applying a fuzzy classifier system (FCS) to control mobile robots, since the FCS can easily treat continuous inputs, such as sensors and images by using a fuzzy number. By using the FCS, however, only reflective rules are acquired. Thus, in the proposed approach, an additional genetic algorithm is incorporated in order to search for strategic knowledge, i.e., the sequence of effective activated rules in the FCS. Therefore, the proposed method consists of two modules: an ordinal FCS and the genetic algorithm. Computational experiments based on WEBOTS, one of the Khepera robot simulators, confirm the effectiveness of the proposed method</p

On the Behavior of Proposers in Ultimatum Games

We demonstrate that one should not expect convergence of the proposals to the subgame perfect Nash equilibrium offer in standard ultimatum games. First, imposing strict experimental control of the behavior of the receiving players and focusing on the behavior of the proposers, we show experimentally that proposers do not learn to make the expected-payoff-maximizing offer. Second, considering a range of learning theories (from optimal to boundedly rational), we explain that this is an inherent feature of the learning task faced by the proposers, and we provide some insights into the actual learning behavior of the experimental subjects. This explanation for the lack of convergence to the subgame perfect Nash equilibrium in ultimatum games complements most alternative explanations.Ultimatum game, Non-equilibrium behavior, Laboratory experiment, Multi-armed bandit, Optimal learning, Gittins index, Bounded rationality

On the Behavior of Proposers in Ultimatum Games

We demonstrate that one should not expect convergence of the proposals to the subgame perfect Nash equilibrium offer in standard ultimatum games. First, imposing strict experimental control of the behavior of the receiving players and focusing on the behavior of the proposers, we show experimentally that proposers do not learn to make the expected-payoff-maximizing offer. Second, considering a range of learning theories (from optimal to boundedly rational), we explain that this is an inherent feature of the learning task faced by the proposers, and we provide some insights into the actual learning behavior of the experimental subjects. This explanation for the lack of convergence to the subgame perfect Nash equilibrium in ultimatum games complements most alternative explanations.Ultimatum game, Non-equilibrium behavior, Laboratory experiment, Multi-armed bandit, Optimal learning, Gittins index, Bounded rationality

Intelligent data mining via evolutionary computing

Master'sMASTER OF ENGINEERIN

Transitions as a coevolutionary process: The urban emergence of electric vehicle inventions

This paper combines a multi-sectoral approach with a perspective on the geography of transitions. The concept of coevolution is used to bridge these contributions as it allows to see mutual influences and adaptation between sectors while acknowledging spatial embeddedness and its economic, institutional and social aspects. The argument is discussed using the case of the transition to Electric Vehicles (EVs) and the connections between three technologies: EV, battery, and smart grid. Patent citations are used to construct three main paths allowing to geolocate key inventions and to elaborate on the role of cities in supporting knowledge recombination. The case study suggests that a coevolutionary perspective can contribute to understanding the geography of transitions in three ways: by relating emerging socio-technical configurations to changed power relations and opportunities along the value chain, by exposing the spatial embeddedness of interdependent sectors and by clarifying the role of actors and networks

Coevolutionary Genetic Algorithms for Establishing Nash Equilibrium in Symmetric Cournot Games

We use co-evolutionary genetic algorithms to model the players' learning process in several Cournot models, and evaluate them in terms of their convergence to the Nash Equilibrium. The \social-learning" versions of the two co-evolutionary algorithms we introduce, establish Nash Equilibrium in those models, in contrast to the \individual learning" versions which, as we see here, do not imply the convergence of the players' strategies to the Nash outcome. When players use \canonical co-evolutionary genetic algorithms" as learning algorithms, the process of the game is an ergodic Markov Chain, and therefore we analyze simulation results using the relevant methodology, to find that in the \social" case, states leading to NE play are highly frequent at the stationary distribution of the chain, in contrast to the \individual learning" case, when NE is not reached at all in our simulations; to ftnd that the expected Hamming distance of the states at the limiting distribution from the \NE state" is significantly smaller in the \social" than in the \individual learning case"; to estimate the expected time that the \social" algorithms need to get to the \NE state" and verify their robustness and finally to show that a large fraction of the games played are indeed at the Nash Equilibrium.Genetic Algorithms, Cournot oligopoly, Evolutionary Game Theory, Nash Equilibrium

Selection pressure and organizational cognition: implications for the social determinants of health

We model the effects of Schumperterian 'selecton pressures' -- in particular Apartheid and the neoliberal 'market economy' -- on organizational cognition in minority communities, given the special role of culture in human biology. Our focus is on the dual-function social networks by which culture is imposed and maintained on individuals and by which immediate patterns of opportunity and threat are recognized and given response. A mathematical model based on recent advances in complexity theory displays a joint cross-scale linkage of social, individual central nervous system, and immune cognition with external selection pressure through mixed and synergistic punctuated 'learning plateaus.' This provides a natural mechanism for addressing the social determinants of health at the individual level. The implications of the model, particularly the predictions of synergistic punctuation, appear to be empirically testable