Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Efficient reinforcement learning through symbiotic evolution
Machine Learning - Special issue on reinforcement learning
Niching methods for genetic algorithms
Niching methods for genetic algorithms
A Cooperative Coevolutionary Approach to Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
A Comparison of Optimization Techniques for Integrated Manufacturing Planning and Scheduling
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Spatially Structured Evolutionary Algorithms: Artificial Evolution in Space and Time (Natural Computing Series)
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Using genetic algorithms to explore pattern recognition in the immune system
Evolutionary Computation
Implicit niching in a learning classifier system: Nature's way
Evolutionary Computation
Artificial ecosystem selection for evolutionary optimisation
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
The microbial genetic algorithm
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part II
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When niching or speciation is required to perform a task that has several different component parts, standard genetic algorithms GAs struggle. They tend to evaluate and select all individuals on the same part of the task, which leads to genetic convergence within the population. The goal of evolutionary niching methods is to enforce diversity in the population so that this genetic convergence is avoided. One drawback with some of these niching methods is that they require a priori knowledge or assumptions about the specific fitness landscape in order to work; another is that many such methods are not set up to work on cooperative tasks where fitness is only relevant at the group level. Here we address these problems by presenting the group GA, described earlier by the authors, which is a group-based evolutionary algorithm that can lead to emergent niching. After demonstrating the group GA on an immune system matching task, we extend the previous work and present two modified versions where the number of niches does not need to be specified ahead of time. In the random-group-size GA, the number of niches is varied randomly during evolution, and in the evolved-group-size GA the number of niches is optimized by evolution. This provides a framework in which we can evolve groups of individuals to collectively perform tasks with minimal a priori knowledge of how many subtasks there are or how they should be shared out.