Analyzing probabilistic models in hierarchical BOA on traps and spin glasses
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Towards billion-bit optimization via a parallel estimation of distribution algorithm
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Substructural Surrogates for Learning Decomposable Classification Problems
Learning Classifier Systems
Analyzing probabilistic models in hierarchical BOA
IEEE Transactions on Evolutionary Computation - Special issue on evolutionary algorithms based on probabilistic models
A new DSM clustering algorithm for linkage groups identification
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Learning factorizations in estimation of distribution algorithms using affinity propagation
Evolutionary Computation
A Markovianity based optimisation algorithm
Genetic Programming and Evolvable Machines
Factor graph based factorization distribution algorithm
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Unlike most simple textbook examples, the real world is full with complex systems, and researchers in many different fields are often confronted by problems arising from such systems. Simple heuristics or even enumeration works quite well on small and easy problems; however, to efficiently solve large and difficult problems, proper decomposition according to the complex system is the key. In this research project, investigating and analyzing interactions between components of complex systems shed some light on problem decomposition. By recognizing three bare-bone types of interactions---modularity, hierarchy, and overlap, theories and models are developed to dissect and inspect problem decomposition in the context of genetic algorithms. This dissertation presents a research project to develop a competent optimization method to solve boundedly difficult problems with modularity, hierarchy, and overlap by explicit problem decomposition. The proposed genetic algorithm design utilizes a matrix representation of an interaction graph to analyze and decompose the problem. The results from this thesis should benefit research both technically and scientifically. Technically, this thesis develops an automated dependency structure matrix clustering technique and utilizes it to design a competent black-box problem solver. Scientifically, the explicit interaction model better describes the problem structure and helps researchers gain important insights through the explicitness of the procedure.