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Genetic Algorithms in Search, Optimization & Machine Learning

01 Jan 1989-
About: The article was published on 1989-01-01 and is currently open access. It has received 2298 citations till now.
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Journal ArticleDOI
Rainer Storn1, Kenneth Price
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
Abstract: A new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented. By means of an extensive testbed it is demonstrated that the new method converges faster and with more certainty than many other acclaimed global optimization methods. The new method requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.

24,053 citations

Journal ArticleDOI
01 Feb 1996
TL;DR: It is shown how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling, and the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.
Abstract: An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.

11,224 citations

Book
John R. Koza1
01 Jan 1994
TL;DR: This book presents a method to automatically decompose a program into solvable components, called automatically defined functions (ADF), and then presents case studies of the application of this method to a variety of problems.
Abstract: This book is a followon to the book in which John Koza introduced genetic programming (GP) to the world "enetic Programming: On the Programming of Computers by Means of Nataral Selection " [5] 1. As such, the primary intended audience is someone already familiar with GP; however, Koza does provide introductory material to both genetic algorithms (GA) and GP. The driving force behind this book is a method to automatically decompose a program into solvable components. The book presents this method, called automatically defined functions (ADF), and then presents case studies of the application of this method to a variety of problems. While this book's size is intimidating, there is a wealth of information to be found by the reader willing to conduct a prolonged campaign. The reader is advised to study the first seven chapters of the book to gain an understanding of the concepts behind ADFs. Then the reader should be able to select which case studies he or she finds to be of interest. If the reader is feeling overwhelmed by the information presented in the book, there are several concise chapters dealing with ADFs by Koza and others in Advances in Genetic Programming edited by Kenneth Kinnear [3] 2.

1,842 citations


Cites background from "Genetic Algorithms in Search, Optim..."

  • ...Genetic programming is an offshoot of GA [1,2]....

    [...]

Journal ArticleDOI
TL;DR: This paper systematically review and analyze many problems from the EA literature, each belonging to the important class of real-valued, unconstrained, multiobjective test problems, and presents a flexible toolkit for constructing well-designed test problems.
Abstract: When attempting to better understand the strengths and weaknesses of an algorithm, it is important to have a strong understanding of the problem at hand. This is true for the field of multiobjective evolutionary algorithms (EAs) as it is for any other field. Many of the multiobjective test problems employed in the EA literature have not been rigorously analyzed, which makes it difficult to draw accurate conclusions about the strengths and weaknesses of the algorithms tested on them. In this paper, we systematically review and analyze many problems from the EA literature, each belonging to the important class of real-valued, unconstrained, multiobjective test problems. To support this, we first introduce a set of test problem criteria, which are in turn supported by a set of definitions. Our analysis of test problems highlights a number of areas requiring attention. Not only are many test problems poorly constructed but also the important class of nonseparable problems, particularly nonseparable multimodal problems, is poorly represented. Motivated by these findings, we present a flexible toolkit for constructing well-designed test problems. We also present empirical results demonstrating how the toolkit can be used to test an optimizer in ways that existing test suites do not

1,567 citations


Cites background from "Genetic Algorithms in Search, Optim..."

  • ...Although disconnected Pareto optimal sets usually map to disconnected Pareto optimal fronts, this is not always the case....

    [...]

Journal ArticleDOI
TL;DR: A problem-specific genetic algorithm (GA) is developed and demonstrated to analyze series-parallel systems and to determine the optimal design configuration when there are multiple component choices available for each of several k-out-of-n:G subsystems.
Abstract: A problem-specific genetic algorithm (GA) is developed and demonstrated to analyze series-parallel systems and to determine the optimal design configuration when there are multiple component choices available for each of several k-out-of-n:G subsystems. The problem is to select components and redundancy-levels to optimize some objective function, given system-level constraints on reliability, cost, and/or weight. Previous formulations of the problem have implicit restrictions concerning the type of redundancy allowed, the number of available component choices, and whether mixing of components is allowed. GA is a robust evolutionary optimization search technique with very few restrictions concerning the type or size of the design problem. The solution approach was to solve the dual of a nonlinear optimization problem by using a dynamic penalty function. GA performs very well on two types of problems: (1) redundancy allocation originally proposed by Fyffe, Hines, Lee, and (2) randomly generated problem with more complex k-out-of-n:G configurations.

777 citations