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—This paper introduces the compact genetic algorithm (cGA) which represents the population as a probability distribution over the set of solutions and is operationally equivalent to the order-one behavior of the simple GA with uniform crossover. It processes each gene independently and requires less memory than the simple GA. The development of the compact… (More)

This paper summarizes the research on population-based probabilistic search algorithms based on modeling promising solutions by estimating their probability distribution and using the constructed model to guide the further exploration of the search space. It settles the algorithms in the eld of genetic and evolutionary computation where they have been… (More)

From the user's point of view, setting the parameters of a genetic algorithm (GA) is far from a trivial task. Moreover, the user is typically not interested in population sizes, crossover probabilities, selection rates, and other GA technicalities. He is just interested in solving a problem, and what he would really like to do, is to hand-in the problem to… (More)

This paper explores an idealized dynamic population sizing strategy for solving additive decomposable problems of uniform scale. The method is designed on top of the foundations of existing population sizing theory for this class of problems, and is carefully compared with an optimal fixed population sized genetic algorithm. The resulting strategy should be… (More)

- Thomas Philip Runarsson, Hans-Georg Beyer, Edmund Burke, Juan J. Merelo-Guervös, L. Darreil Whitley, Xin Yao +37 others
- 2007

| There are several issues that need to be taken in consideration when designing a hybrid problem solver. This paper focuses on one of them|decision making. More speciically, we address the following questions: given two diierent methods, how to get the most out of both of them? When should we use one and when should we use the other in order to get maximum… (More)

Over the last 10 years, many efforts have been made to design a competent genetic algorithm. This paper revisits and extends the latest of such efforts— the linkage learning genetic algorithm. Specifically, it introduces an efficient mechanism for representing the non-coding material. Recent investigations suggest that this new method is crucial for solving… (More)

This paper studies the utility of using substructural neighborhoods for local search in the Bayesian optimization algorithm (BOA). The probabilistic model of BOA, which automatically identifies important problem substructures, is used to define the structure of the neighborhoods used in local search. Additionally, a surrogate fitness model is considered to… (More)