Genetic Algorithms and the Optimal Allocation of Trials

Holland, John H.

doi:10.1137/0202009

Cited by 1,033 publications

(472 citation statements)

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“…The evolutionary computation scientific community has a new class, immune algorithms [4,17], that along with genetic algorithms [24,25], evolution strategies [26], evolutionary programming [27] and the genetic programming [28] constitutes the overall set of evolutionary algorithms.…”

Section: Discussionmentioning

confidence: 99%

Clonal Selection Algorithms: A Comparative Case Study Using Effective Mutation Potentials

Cutello

Narzisi

Nicosia

et al. 2005

Lecture Notes in Computer Science

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Abstract. This paper presents a comparative study of two important Clonal Selection Algorithms (CSAs): CLONALG and opt-IA. To deeply understand the performance of both algorithms, we deal with four different classes of problems: toy problems (one-counting and trap functions), pattern recognition, numerical optimization problems and NP-complete problem (the 2D HP model for protein structure prediction problem). Two possible versions of CLONALG have been implemented and tested. The experimental results show a global better performance of opt-IA with respect to CLONALG. Considering the results obtained, we can claim that CSAs represent a new class of Evolutionary Algorithms for effectively performing searching, learning and optimization tasks.

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Section: Discussionmentioning

confidence: 99%

Clonal Selection Algorithms: A Comparative Case Study Using Effective Mutation Potentials

Cutello

Narzisi

Nicosia

et al. 2005

Lecture Notes in Computer Science

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“…One example is genetic algorithms (GA) [8]. Genetic algorithms are a well-known optimization technique which happens to have, in turn, been inspired by biology.…”

Section: Topics Coveredmentioning

confidence: 99%

A taxonomy of biologically inspired research in computer networking

2010

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a b s t r a c tThe natural world is enormous, dynamic, incredibly diverse, and highly complex. Despite the inherent challenges of surviving in such a world, biological organisms evolve, self-organize, self-repair, navigate, and flourish. Generally, they do so with only local knowledge and without any centralized control. Our computer networks are increasingly facing similar challenges as they grow larger in size, but are yet to be able to achieve the same level of robustness and adaptability. Many research efforts have recognized these parallels, and wondered if there are some lessons to be learned from biological systems. As a result, biologically inspired research in computer networking is a quickly growing field. This article begins by exploring why biology and computer network research are such a natural match. We then present a broad overview of biologically inspired research, grouped by topic, and classified in two ways: by the biological field that inspired each topic, and by the area of networking in which the topic lies. In each case, we elucidate how biological concepts have been most successfully applied. In aggregate, we conclude that research efforts are most successful when they separate biological design from biological implementation -that is to say, when they extract the pertinent principles from the former without imposing the limitations of the latter.

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“…Solving the NP complete problem only to be able to depend on the approximate method, many scholars have conducted the research regarding this. In this thesis we will discuss and evaluate the performance under the unified test platform to the commonly used TTP algorithm [4][5] .…”

Section: Introductionmentioning

confidence: 99%

The Application Research of Improved Bat Algorithm for Time Table Problem

Liu¹

2016

Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology

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Abstract:The time table problem is a typical NP complete problem, when the bat algorithm solves this problem; it will quickly converge, and can get the local optimal solution but not the global optimal solution. Therefore, we use the advantage of active evolutionary operator to improve the bat algorithm, and to solve TTP problem. We do a lot of experiments and evaluate the performance of the improved bat algorithm. The experiment results show that improved bat algorithm is a more superior algorithm to apply to the TTP problem.

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Genetic Algorithms and the Optimal Allocation of Trials

Cited by 1,033 publications

References 3 publications

Clonal Selection Algorithms: A Comparative Case Study Using Effective Mutation Potentials

Clonal Selection Algorithms: A Comparative Case Study Using Effective Mutation Potentials

A taxonomy of biologically inspired research in computer networking

The Application Research of Improved Bat Algorithm for Time Table Problem

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