An Introduction to Genetic Algorithms.

doi:10.1162/ARTL.1997.3.1.63

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An Introduction to Genetic Algorithms.

Journal Article•DOI•

An Introduction to Genetic Algorithms.

01 Jan 1997-Artificial Life (MITP)-Vol. 3, Iss: 1, pp 63-65

TL;DR: An Introduction to Genetic Algorithms as discussed by the authors is one of the rare examples of a book in which every single page is worth reading, and the author, Melanie Mitchell, manages to describe in depth many fascinating examples as well as important theoretical issues.

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Abstract: An Introduction to Genetic Algorithms is one of the rare examples of a book in which every single page is worth reading. The author, Melanie Mitchell, manages to describe in depth many fascinating examples as well as important theoretical issues, yet the book is concise (200 pages) and readable. Although Mitchell explicitly states that her aim is not a complete survey, the essentials of genetic algorithms (GAs) are contained: theory and practice, problem solving and scientific models, a \"Brief History\" and \"Future Directions.\" Her book is both an introduction for novices interested in GAs and a collection of recent research, including hot topics such as coevolution (interspecies and intraspecies), diploidy and dominance, encapsulation, hierarchical regulation, adaptive encoding, interactions of learning and evolution, self-adapting GAs, and more. Nevertheless, the book focused more on machine learning, artificial life, and modeling evolution than on optimization and engineering.

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Journal Article•DOI•

The Structure and Function of Complex Networks

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Mark Newman

01 Jan 2003-Siam Review

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Cites methods from "An Introduction to Genetic Algorith..."

...The method used is a type of genetic algorithm [285] or enrichment method [180] that in its simplest form has a number of “agents” that start crawling the Web at random, looking for pages that contain, for example, particular words or sets of words given by the user....
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Book•

Genetic Algorithms

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David E. Goldberg, William Shakespeare

01 Jan 2002

17,039 citations

Cites background from "An Introduction to Genetic Algorith..."

...There are many publications which give excellent introductions to genetic algorithms: see Holland (1975), Davis (1987), Goldberg (1989b), Davis (1991), Beasley et al. (1993), Forrest (1993), Reeves (1995), Michalewicz (1996), Mitchell (1996), Falkenauer (1998), Coley (1999), and Man et al. (1999)....
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Evolutionary algorithms for solving multi-objective problems

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Gary B. Lamont, David A. Van Veldhuizen

30 Jun 2002

TL;DR: This paper presents a meta-anatomy of the multi-Criteria Decision Making process, which aims to provide a scaffolding for the future development of multi-criteria decision-making systems.

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Abstract: List of Figures. List of Tables. Preface. Foreword. 1. Basic Concepts. 2. Evolutionary Algorithm MOP Approaches. 3. MOEA Test Suites. 4. MOEA Testing and Analysis. 5. MOEA Theory and Issues. 3. MOEA Theoretical Issues. 6. Applications. 7. MOEA Parallelization. 8. Multi-Criteria Decision Making. 9. Special Topics. 10. Epilog. Appendix A: MOEA Classification and Technique Analysis. Appendix B: MOPs in the Literature. Appendix C: Ptrue & PFtrue for Selected Numeric MOPs. Appendix D: Ptrue & PFtrue for Side-Constrained MOPs. Appendix E: MOEA Software Availability. Appendix F: MOEA-Related Information. Index. References.

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5,994 citations

Cites background from "An Introduction to Genetic Algorith..."

...• MOMGA & MOMGA II: Although messy GAs are very powerful, their main disadvantages are related to the exponential growth of their population as the size of the building blocks grows (Mitchell, 1996)....
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...This is intended to produce distinct "species" (mating groups) in the population (Mitchell, 1996)....
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Principles of data mining

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David J. Hand, Heikki Mannila, Padhraic Smyth

01 Jan 2001

TL;DR: This paper gives a lightning overview of data mining and its relation to statistics, with particular emphasis on tools for the detection of adverse drug reactions.

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Abstract: The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.

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Abstract: General Strategies.- Regression Models.- Classification Models.- Other Considerations.- Appendix.- References.- Indices.

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3,672 citations

Cites background from "An Introduction to Genetic Algorith..."

...tree (oblique trees), partDSA (for the model of Molinaro et al. (2010)), and evtree (trees developed using genetic algorithms)....
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...Other approaches such as genetic algorithms (Mitchell 1998) or simplex search methods (Olsson and Nelson 1975) can also find optimal tuning parameters....
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