Genetic Algorithms in Search

Genetic Algorithms in Search

01 Jan 1989-pp 192-208

About: The article was published on 1989-01-01 and is currently open access. It has received 12457 citations till now. The article focuses on the topics: Quality control and genetic algorithms & Genetic representation.

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Journal Article•DOI•

Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

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Rainer Storn¹, Kenneth Price•Institutions (1)

Siemens¹

01 Dec 1997-Journal of Global Optimization

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.

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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.

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24,053 citations

Journal Article•DOI•

Deep learning in neural networks

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Jürgen Schmidhuber¹•Institutions (1)

University of Lugano¹

01 Jan 2015-Neural Networks

TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.

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14,635 citations

Cites background from "Genetic Algorithms in Search"

...RL NNs can also be evolved through Evolutionary Algorithms (EAs) (Fogel, Owens, & Walsh, 1966; Goldberg, 1989; Holland, 1975; Rechenberg, 1971; Schwefel, 1974) in a series of trials....
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Journal Article•DOI•

Data clustering: a review

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Anil K. Jain¹, M. N. Murty², Patrick J. Flynn³•Institutions (3)

Michigan State University¹, Indian Institute of Science², Ohio State University³

01 Sep 1999-ACM Computing Surveys

TL;DR: An overview of pattern clustering methods from a statistical pattern recognition perspective is presented, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners.

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Abstract: Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.

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14,054 citations

Cites background or methods from "Genetic Algorithms in Search"

...Using this representation permits them to map the clustering problem into a permutation problem such as the traveling salesman problem, which can be solved by using the permutation crossover operators [Goldberg 1989]....
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...The best-known evolutionary techniques are genetic algorithms (GAs) [Holland 1975; Goldberg 1989], evolution strategies (ESs) [Schwefel 1981], and evolutionary programming (EP) [Fogel et al. 1965]....
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Journal Article•DOI•

Ant system: optimization by a colony of cooperating agents

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Marco Dorigo, Vittorio Maniezzo¹, Alberto Colorni²•Institutions (2)

University of Bologna¹, Polytechnic University of Milan²

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.

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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.

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11,224 citations

Cites background from "Genetic Algorithms in Search"

...algorithms [ 19 ]) for the modified algorithm in which at every cycle the trail laid on the edges belonging to the...
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Journal Article•DOI•

Comparison of Multiobjective Evolutionary Algorithms: Empirical Results

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Eckart Zitzler¹, Kalyanmoy Deb², Lothar Thiele¹•Institutions (2)

ETH Zurich¹, Indian Institute of Technology Kanpur²

01 Jun 2000-Evolutionary Computation

TL;DR: This paper provides a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions and shows that elitism is shown to be an important factor for improving evolutionary multiobjectives search.

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Abstract: In this paper, we provide a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions. Each test function involves a particular feature that is known to cause difficulty in the evolutionary optimization process, mainly in converging to the Pareto-optimal front (e.g., multimodality and deception). By investigating these different problem features separately, it is possible to predict the kind of problems to which a certain technique is or is not well suited. However, in contrast to what was suspected beforehand, the experimental results indicate a hierarchy of the algorithms under consideration. Furthermore, the emerging effects are evidence that the suggested test functions provide sufficient complexity to compare multiobjective optimizers. Finally, elitism is shown to be an important factor for improving evolutionary multiobjective search.

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4,867 citations

Cites methods from "Genetic Algorithms in Search"

...Finally, Pareto selection makes direct use of the dominance relation from Definition 1; Goldberg (1989) was the first to suggest a Pareto-based fitness assignment strategy....
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...…of generations : 250 Population size : 100 Crossover rate : 0.8 Mutation rate : 0.01 Niching parameter share : 0.48862 Domination pressure dom : 10 The niching parameter was calculated using the guidelines given in Deb and Goldberg (1989) assuming the formation of ten independent niches....
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...Finally, Pareto selection makes direct use of the dominance relation from Definition 1; Goldberg (1989) was the first to suggest a Pareto-based fitness assignment strategy....
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Genetic Algorithms in Search

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