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Finite Markov Chain Results in Evolutionary Computation: A Tour d'Horizon
, 1998
"... . The theory of evolutionary computation has been enhanced rapidly during the last decade. This survey is the attempt to summarize the results regarding the limit and finite time behavior of evolutionary algorithms with finite search spaces and discrete time scale. Results on evolutionary algorithms ..."
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Cited by 61 (2 self)
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. The theory of evolutionary computation has been enhanced rapidly during the last decade. This survey is the attempt to summarize the results regarding the limit and finite time behavior of evolutionary algorithms with finite search spaces and discrete time scale. Results on evolutionary algorithms beyond finite space and discrete time are also presented but with reduced elaboration. Keywords: evolutionary algorithms, limit behavior, finite time behavior 1. Introduction The field of evolutionary computation is mainly engaged in the development of optimization algorithms which design is inspired by principles of natural evolution. In most cases, the optimization task is of the following type: Find an element x 2 X such that f(x ) f(x) for all x 2 X , where f : X ! IR is the objective function to be maximized and X the search set. In the terminology of evolutionary computation, an individual is represented by an element of the Cartesian product X \Theta A, where A is a possibly...
A GameTheoretic Approach to the Simple Coevolutionary Algorithm
 Proceedings of the Sixth International Conference on Parallel Problem Solving from Nature (PPSN VI
"... The fundamental distinction between ordinary evolutionary algorithms (EA) and coevolutionary algorithms lies in the interaction between coevolving entities. We believe that this property is essentially gametheoretic in nature. Using game theory, we describe extensions that allow familiar mixingma ..."
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Cited by 58 (9 self)
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The fundamental distinction between ordinary evolutionary algorithms (EA) and coevolutionary algorithms lies in the interaction between coevolving entities. We believe that this property is essentially gametheoretic in nature. Using game theory, we describe extensions that allow familiar mixingmatrix and Markovchain models of EAs to address coevolutionary algorithm dynamics. We then employ concepts from evolutionary game theory to examine design aspects of conventional coevolutionary algorithms that are poorly understood.
The nature of niching: genetic algorithms and the evolution of optimal, cooperative populations
, 1997
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Cournot or Walras? Agent Based Learning, Rationality, and Long Run Results in Oligopoly Games
, 2002
"... Recent literature shows that learning in oligopoly games might in the long run result in the Cournot or in the Walras equilibrium. Which outcome is achieved seems to depend on the underlying learning dynamics. This paper analyzes the forces behind the learning mechanisms determining the long run out ..."
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Cited by 5 (0 self)
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Recent literature shows that learning in oligopoly games might in the long run result in the Cournot or in the Walras equilibrium. Which outcome is achieved seems to depend on the underlying learning dynamics. This paper analyzes the forces behind the learning mechanisms determining the long run outcome. Apart from the fact that there is a difference between social and individual learning, the key parameter is shown to be the degree of rationality of the learning agents: Learning the Cournot strategy requires the agents to acquire a large amount of information and to use sophisticated computational techniques, while the Walras strategy can be shown to be a particular ‘low rationality result‘.
An Analysis on Crossovers for Real Number Chromosomes in an Infinite Population Size
 Proc. International Joint Conference on Artificial Intelligence (IJCAI97
, 1997
"... In this paper, as one approach for mathematical analysis of evolutionary algorithms with real number chromosomes, we focus our attention on crossovers, give a general framework of the description for the change of the distribution of the population through them, and verify the properties of crossove ..."
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Cited by 1 (0 self)
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In this paper, as one approach for mathematical analysis of evolutionary algorithms with real number chromosomes, we focus our attention on crossovers, give a general framework of the description for the change of the distribution of the population through them, and verify the properties of crossovers based on the framework. This framework includes various crossover which have been proposed and we apply our result to these crossover methods. 1 Introduction A lot of experimental and theoretical researches on Evolutionary Algorithms (EA) have been recently reported. In the theoretical results, most of them are ones for EAs using bit strings as chromosomes, in particular, the Simple Genetic Algorithms (SGA). These are based on the theory of Finite Markov Chain [ Dawid, 1994; Davis and Principe, 1993; Nix and Vose, 1992; Rudolph, 1994 ] because the SGA uses bit strings with a constant length. However, the state spaces of EAs using real number chromosomes are infinite and uncountable sets...
Journal of Economic Dynamics & Control
, 2000
"... Genetic algorithm learning and evolutionary ..."
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“An Exact ConsumptionLoan Model of Interest With or Without the Social Contrivance of Money ” appears
"... the Social Contrivance of Money ” as an exemplary, Neoclassically misconstructed model of nowadays society and its economic activities. The failure to notice the existence of unpaid labor in the framework of the model, and the implicit dependence of the outcomes on unpaid (omitted) reproduction wor ..."
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the Social Contrivance of Money ” as an exemplary, Neoclassically misconstructed model of nowadays society and its economic activities. The failure to notice the existence of unpaid labor in the framework of the model, and the implicit dependence of the outcomes on unpaid (omitted) reproduction work has apparently gone unchallenged so far. The logical inconsistencies of the model and their androcentric backgrounds are discussed in this paper. Furthermore, we aim to debate (but not explicitly formulate) an alternative modeling approach based on the use of Genetic Algorithms to include – at least some – crucial features of modern society in its whole heterogeneity.
Learning and Adaptive Artificial Agents: Analysis of an evolutionary economic model
, 2000
"... We study a simple overlapping generations economy as an adaptive learning system. The learning is via a socalled genetic algorithm process. We first investigate performances of Holland’s standard GA (SGA), Arifovic’s augmented GA (AGA), and Birchenhall’s selective transfer GA (STGA), Bullard and Du ..."
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We study a simple overlapping generations economy as an adaptive learning system. The learning is via a socalled genetic algorithm process. We first investigate performances of Holland’s standard GA (SGA), Arifovic’s augmented GA (AGA), and Birchenhall’s selective transfer GA (STGA), Bullard and Duffy (BDGA) as a model of population learning. In addition, we also investigate these learning algorithms variant. Second, compared to population learning, we also implement the GAs as a model of individual learning. An “ecological ” approach showing “intergeneration ” aspect of the GA to learning problems is therefore modelled. Finally, We visit a further approach called “open learning” model, about endogenising learning in which agents learn how to learn. The results we obtain confirm previous statement that the stability of the Pareto superior equilibrium of the model, i.e. the low inflation equilibrium, is robust independent of precise learning variant. Furthermore, we show that individual agents with heterogeneous learning schemes eventually coordinate on the equilibrium. We offer the interpretation of convergence to the equilibrium. 1 I.
ACEmodel: A Conceptual Evolutionary model for Evolutionary Computation and Artificial Life
, 2002
"... Danvinim Evolutionary system a system satisfying the abstract conditions: reproduction with heritable variation, in a finite world, giving rise to Natuml Selection encompasses a complex and subtle system of interrelated theories, whose substantive transplantation to any artificial medium let it b ..."
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Danvinim Evolutionary system a system satisfying the abstract conditions: reproduction with heritable variation, in a finite world, giving rise to Natuml Selection encompasses a complex and subtle system of interrelated theories, whose substantive transplantation to any artificial medium let it be mathematical model or computational model will be very far from easy. There are two motives in bringing Darwinian evolution into computational frameworks: one to understand the Darwinian evolution, and the other is to view Darwinian evolution that carries out controlledadaptivestochastic search in the space of all possible DNAsequences for emergence and improvement of the living beings on our planet as an optimization process, which can be simulated in appropriate frameworks to solve some intractable problems. The first motive led to emerging field of study commonly referred to as Artificial Life, and other gave way to emergence of Evolutionary Computation which is speculated to be the only practical path to the development of ontogenetic machine intelligence. In this thesis we touch upon all the above aspects. Natural selection is the central concept of Darwinian evolution and hence capturing natural selection in computational frameworks which maintains the spirit of Darwinian evolution in the sense of conventional, terrestrial and biological perspectives is essential. Naive models of evo