Cooperative Co-evolution of Multi-agents
Proceedings of the Joint JSAI 2001 Workshop on New Frontiers in Artificial Intelligence
Adaptive dynamic load-balancing through evolutionary formation of coalitions
Design and application of hybrid intelligent systems
Knowledge and Information Systems
Self-Organized Modularization in Evolutionary Algorithms
Evolutionary Computation
Evolving Neural Network Ensembles by Minimization of Mutual Information
International Journal of Hybrid Intelligent Systems
Heuristic speciation for evolving neural network ensemble
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Expert Systems with Applications: An International Journal
An improved approach to find membership functions and multiple minimum supports in fuzzy data mining
Expert Systems with Applications: An International Journal
Co-evolutionary learning with strategic coalition for multiagents
Applied Soft Computing
Evolution and incremental learning in the iterated prisoner's dilemma
IEEE Transactions on Evolutionary Computation
Iterated prisoner's dilemma for species
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Discovering several robot behaviors through speciation
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
A simple powerful constraint for genetic programming
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
A grey-box approach to automated mechanism design
Electronic Commerce Research and Applications
Blind signal separation through cooperating ANNs
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Evolution of cooperating ANNs through functional phenotypic affinity
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Co-evolutionary automatic programming for software development
Information Sciences: an International Journal
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Many natural and artificial systems use a modular approach to reduce the complexity of a set of subtasks while solving the overall problem satisfactorily. There are two distinct ways to do this. In functional modularization, the components perform very different tasks, such as subroutines of a large software project. In categorical modularization, the components perform different versions of basically the same task, such as antibodies in the immune system. This second aspect is the more natural for acquiring strategies in games of conflict, An evolutionary learning system is presented which follows this second approach to automatically create a repertoire of specialist strategies for a game-playing system. This relieves the human effort of deciding how to divide and specialize. The genetic algorithm speciation method used is one based on fitness sharing. The learning task is to play the iterated prisoner's dilemma. The learning system outperforms the tit-for-tat strategy against unseen test opponents. It learns using a “black box” simulation, with minimal prior knowledge of the learning task