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- David E. Goldberg
- AI Magazine
- 1989

This book brings together-in an informal and tutorial fashion-the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems...

- Jeffrey Horn, Nicholas Nafpliotis, David E. Goldberg
- International Conference on Evolutionary…
- 1994

| 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)

- David E. Goldberg, Jon T. Richardson
- ICGA
- 1987

In this paper an algorithm based on the concepts of genetic algorithms that uses an estimation of a probability distribution of promising solutions in order to generate new candidate solutions is proposed To esti mate the distribution techniques for model ing multivariate data by Bayesian networks are used The proposed algorithm identi es reproduces and… (More)

- David E. Goldberg, Kalyanmoy Deb
- FOGA
- 1990

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)

- David E. Goldberg, Robert Lingle
- ICGA
- 1985

- David E. Goldberg, Bradley Korb, Kalyanmoy Deb
- Complex Systems
- 1989

- Georges R. Harik, Fernando G. Lobo, David E. Goldberg
- IEEE Trans. Evolutionary Computation
- 1999

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)

- Kalyanmoy Deb, David E. Goldberg
- ICGA
- 1989

- Martin Pelikan, David E. Goldberg, Fernando G. Lobo
- Comp. Opt. and Appl.
- 2002

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)