Evolutionary Computation
Technical Comittee:
Evolutionary
Algorithms based on Probabilistic Models Working Group
Introduction
Evolutionary algorithms based on probabilistic models (EAPM) have been recognized as a new computing paradigm in evolutionary computation. Instances of EAPMs include probabilistic model building genetic algorithms, estimation of distribution algorithms, ant colony optimization, and cross entropy method, to name a few.
There is no traditional crossover or mutation in EAPMs. Instead, they explicitly extract global statistical information from their previous search and build a probability distribution model of promising solutions, based on the extracted information. Then, new solutions are sampled from the model thus built.
EAPMs represent a new systematic way to solve hard search and optimization problems. The last decade has seen growing interest in this area. As an interdisciplinary research area, the development of EAPMs needs joint efforts from the researchers and practitioners in evolutionary computation, machine learning, statistics and simulation. Therefore, this working group aims at promoting the research and application of EAPMs by joining the knowledge and experiences from the involved areas. To be precise, the main issues to be studied by the group are the following:
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Theory of EAPMs.
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New algorithms.
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Combination of machine learning techniques and EAPMs.
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Combination of statistics techniques and EAPMs.
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Combination of other heuristics and EAPMs.
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EAPMs for multiobjective optimization problems.
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EAPMs in dynamic environments.
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Parallel implementation of EAPMs.
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Real-world/novel applications.
Last updated: 01/24/05.