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Proposition of Competitive Co-evolution Algorithm with Packaging Solutions Method
Abstract
Competitive co-evolution algorithm can adaptively acquire solutions of a problem. However, in a problem which does not have the optimal solution, it needs to decide a set of effective solutions as the best solution. In this paper, we propose a competitive co-evolution algorithm with a packaging solutions to solve the problem. Our algorithm has two characteristics. The one is minimization of the number of individuals in the set by extraction of the complemental solutions. The other is evaluating solutions in some continued generations by setting a life-time to an individual. We apply the proposal method to the Game in order to investigate its effectiveness. Furthermore, we analyze the process of the set formation. In the simulation results, our method can acquire the complemental strategies and shows a better performance than a conventional method.
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One of the important issues in the field of artificial
intelligence is to develop the method on the acquisition of game
strategy. In this paper, our purpose is the acquisition of game-strategy
by using competitive co-evolution approach as a search method. The
competitive co-evolution is the mechanism of the interactive
improvement. However, in practical game, it isn't easy to acquire the
best strategy by applying competitive co-evolution model, because of
complex strategy space. Therefore, to design the acquisition system of
stronger game strategy, we propose an improved competitive co-evolution
model that introduces the concept of “package” as a set of
good strategies. Creating the good package needs to collect some good
strategies to defeat various kinds of the opponents. To show the
efficiency of this algorithm, we apply it to a complicated game
In the simplest scenario of two coevolving populations in competition with each other, fitness progress is achieved at disadvantage of the other population's fitness. The everchanging fitness landscape caused by the competing species (named the "Red Queen effect") makes the system dynamics more complex, but it also provides a set of advantages with respect to single-population evolution. Here we present results from an experiment with two mobile robots, a predator equipped with vision and a much faster prey with simpler sensors. Without anyeffort in fitness design, a set of interesting behaviors emerged in relatively short time, such as obstacle avoidance, straightnavigation, visual tracking, object discrimination (robot vs. wall), object following, and others. Although such experiments cannot yet be performed in real-time on populations of robots for technical reasons, the approach seems promising. 1 CompetitiveCo-Evolution Competitive co-evolution has rece...
Competi tive Co-evolution Model an the Acquisition of Game Strat egy
- M Nerome
- K Yamada
- S Endo
- H Miyagi
M. Nerome, K. Yamada, S. Endo and H. Miyagi: Competi
tive Co-evolution Model an the Acquisition of Game Strat
egy, Lecture Notes in Artificial Intelligence, Springer, pp224231 (1997).