Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Artificial Life: Proceedings of an Interdisciplinary Workshop on the Synthesis and Simulation of Living Systems
Artificial Life II
Knowledge Growth in an Artificial Animal
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Algorithms and Evolution Strategies - Similarities and Differences
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Theory of Self-Reproducing Automata
Theory of Self-Reproducing Automata
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
Learning to control the program evolution process with cultural algorithms
Evolutionary Computation
Evolving 3d morphology and behavior by competition
Artificial Life
Gecko: A continuous 2d world for ecological modeling
Artificial Life
Modeling adaptive autonomous agents
Artificial Life
A Bigger Learning Classifier Systems Bibliography
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
A Study on the Evolution of Learning Classifier Systems
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
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The synthesis of artifacts reproducing behaviors and properties of living beings is one of the main goals of Artificial Life. These artificial entities often evolve according to algorithms based on models of modern genetics. Evolutionary algorithms generally produce micro-evolution in these entities, by applying mutation and crossover on their genotype. The aim of this paper is to present Non-Homogeneous Classifier Systems, NHCS, integrating the process of macro-evolution. A NHCS is a derived type of classical Classifier Systems, CS. In a CS, all classifiers are built on the same structure and own the same properties. With a NHCS, the behavior of artificial creatures is defined by the co-evolution between several differently structured classifiers. These agents, moving in a 2D environment with obstacles and resources, must adapt themselves and breed to build viable populations. Finally, ecological niches and specific behaviors, individual and collective, appear according to initial parameters of agents and environment.