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Evolutionary programming

About: Evolutionary programming is a(n) research topic. Over the lifetime, 6007 publication(s) have been published within this topic receiving 219180 citation(s).

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Papers
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Open accessBook
01 Jan 1992-
Abstract: 1 GAs: What Are They?.- 2 GAs: How Do They Work?.- 3 GAs: Why Do They Work?.- 4 GAs: Selected Topics.- 5 Binary or Float?.- 6 Fine Local Tuning.- 7 Handling Constraints.- 8 Evolution Strategies and Other Methods.- 9 The Transportation Problem.- 10 The Traveling Salesman Problem.- 11 Evolution Programs for Various Discrete Problems.- 12 Machine Learning.- 13 Evolutionary Programming and Genetic Programming.- 14 A Hierarchy of Evolution Programs.- 15 Evolution Programs and Heuristics.- 16 Conclusions.- Appendix A.- Appendix B.- Appendix C.- Appendix D.- References.

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Topics: Genetic programming (54%), Evolutionary programming (53%), Learnable Evolution Model (52%) ...read more

12,058 Citations


Open accessBook
01 Jan 2001-
Abstract: From the Publisher: Evolutionary algorithms are relatively new, but very powerful techniques used to find solutions to many real-world search and optimization problems. Many of these problems have multiple objectives, which leads to the need to obtain a set of optimal solutions, known as effective solutions. It has been found that using evolutionary algorithms is a highly effective way of finding multiple effective solutions in a single simulation run. · Comprehensive coverage of this growing area of research · Carefully introduces each algorithm with examples and in-depth discussion · Includes many applications to real-world problems, including engineering design and scheduling · Includes discussion of advanced topics and future research · Features exercises and solutions, enabling use as a course text or for self-study · Accessible to those with limited knowledge of classical multi-objective optimization and evolutionary algorithms The integrated presentation of theory, algorithms and examples will benefit those working and researching in the areas of optimization, optimal design and evolutionary computing. This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.

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Topics: Evolutionary computation (64%), Evolutionary algorithm (62%), Evolutionary programming (61%) ...read more

11,886 Citations


Open accessJournal ArticleDOI: 10.1016/J.ADVENGSOFT.2013.12.007
Abstract: This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) inspired by grey wolves (Canis lupus). The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, the three main steps of hunting, searching for prey, encircling prey, and attacking prey, are implemented. The algorithm is then benchmarked on 29 well-known test functions, and the results are verified by a comparative study with Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Differential Evolution (DE), Evolutionary Programming (EP), and Evolution Strategy (ES). The results show that the GWO algorithm is able to provide very competitive results compared to these well-known meta-heuristics. The paper also considers solving three classical engineering design problems (tension/compression spring, welded beam, and pressure vessel designs) and presents a real application of the proposed method in the field of optical engineering. The results of the classical engineering design problems and real application prove that the proposed algorithm is applicable to challenging problems with unknown search spaces.

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  • Table 8. Results of composite benchmark functions
    Table 8. Results of composite benchmark functions
  • Fig. 3. 2D and 3D position vectors and their possible next locations
    Fig. 3. 2D and 3D position vectors and their possible next locations
  • Fig. 17. Optimized super cell of BSPCW.
    Fig. 17. Optimized super cell of BSPCW.
  • Table 4. Composite benchmark functions
    Table 4. Composite benchmark functions
  • Fig. 7. 2-D versions of unimodal benchmark functions
    Fig. 7. 2-D versions of unimodal benchmark functions
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Topics: Evolutionary programming (51%), Metaheuristic (51%), Evolution strategy (51%) ...read more

5,531 Citations


Open accessDOI: 10.3929/ETHZ-A-004284029
01 Jan 2001-
Abstract: The Strength Pareto Evolutionary Algorithm (SPEA) (Zitzler and Thiele 1999) is a relatively recent technique for finding or approximating the Pareto-optimal set for multiobjective optimization problems. In different studies (Zitzler and Thiele 1999; Zitzler, Deb, and Thiele 2000) SPEA has shown very good performance in comparison to other multiobjective evolutionary algorithms, and therefore it has been a point of reference in various recent investigations, e.g., (Corne, Knowles, and Oates 2000). Furthermore, it has been used in different applications, e.g., (Lahanas, Milickovic, Baltas, and Zamboglou 2001). In this paper, an improved version, namely SPEA2, is proposed, which incorporates in contrast to its predecessor a fine-grained fitness assignment strategy, a density estimation technique, and an enhanced archive truncation method. The comparison of SPEA2 with SPEA and two other modern elitist methods, PESA and NSGA-II, on different test problems yields promising results.

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4,711 Citations


Open accessJournal ArticleDOI: 10.1162/106365600568202
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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4,455 Citations


Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202138
202032
201955
201883
2017170
2016159

Top Attributes

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Topic's top 5 most impactful authors

Ismail Musirin

89 papers, 489 citations

Xin Yao

63 papers, 8.1K citations

David B. Fogel

59 papers, 7.6K citations

Carlos A. Coello Coello

43 papers, 6.6K citations

Zbigniew Michalewicz

32 papers, 19.5K citations

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