David E. Goldberg

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| Many, if not most, optimization problems have multiple objectives. Historically , multiple objectives have been combined ad hoc to form a scalar objective function , usually through a linear combination (weighted sum) of the multiple attributes, or by turning objectives into constraints. The genetic algorithm (GA), however, is readily modiied to deal with(More)
This paper considers a number of selection schemes commonly used in modern genetic algorithms. Specifically, proportionate reproduction, ranking selection, tournament selection, and Genitor (or «steady state") selection are compared on the basis of solutions to deterministic difference or differential equations, which are verified through computer(More)
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)