Kanji Ueda

A symbiotic evolutionary algorithm for dynamic facility layout problem

A symbiotic evolutionary algorithm (SymEA) for dynamic facility layout problem (DFLP) is developed and tested. SymEA differs from the existing EA approaches for DFLP in two ways: first, it adopted a coevolutionary multi-population approach, second, the generational replacement strategy (/spl mu/, /spl lambda/)-selection of evolution strategies is used to refine the symbiotic relationship.

Evolutionary dynamics of genetic algorithm in the fitness landscape including neutral Networks.

Neutral networks, which are landscapes containing neighboring points of equal fitness, have attracted much research interest in recent years. In this paper, we conduct a series of computer simulations to investigate the effect of an error threshold on the moving speed of a population as well as a variable mutation rate strategy against ruggedness. Two kinds of GA are adopted. One is the simple GA where the mutation rate is constant, and the other is the operon-GA whose effective mutation rate is changing at each locus independently according to the history of the genetic search. The results demonstrate that the moving speed of a population is correlated with the selection pressure as well as the mutation rate. The variable mutation rate strategy is beneficial in the cases of the simplest test function and complex test functions. This tendency becomes clearer with the increase of ruggedness in the test functions.

The Emergence of Cooperation in the Prisoners' Dilemma Network

This paper describes how people establish or fail to establish long-run cooperation in the Prisoners Dilemma Network (PDN), where subjects are allowed to nominate a subject with whom they want to play the Prisoners Dilemma (PD) game at the beginning of each round. We have done a series of experiments with undergraduates and computer simulations of the PDN games. In the experiments most subjects either continued to play the PD game cooperatively with the same partner or never played the PD game cooperatively in the long run, and those who were more cooperative earned more. Since the simulations reproduced the results of the experiments, we can guess the subjects strategies, which were not as apparent and controllable as the programs of agents.