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About 10,000 papers for “Evolutionary algorithm”

Algorithm-Evolutionary

Merelo, Juan J., Castillo, Pedro · Figshare · 2014

A general-purpose module to program standard evolutionary algorithms or to create your own.

Cited by 0Open access

Algorithm-Evolutionary

Merelo, Juan J., Castillo, Pedro · Figshare · 2014

A general-purpose module to program standard evolutionary algorithms or to create your own.

Cited by 0Open access

Evolutionary Algorithm

Yan Ying, Zeng Wen-hua · 2008

Abstract: An advanced hierarchical parallel evolutionary algorithm (AHPEA) is proposed in this paper. To solve the problems caused by “homogeneous subpopulations ” and “synchronous migration ” of the traditional algorithm, AHPEA builds a heterogeneous model and adequately connect subpopulations. Once a subpopulation satisfies the migration conditions, it is allowed to implement the asynchronous migration according to the predetermined migration modes. The experimental results show that, the presented algorithm efficiently solve the conquest and noneffect problems, avoid premature convergence and improve efficiency. Key words: hierarchical parallel evolutionary algorithm; fuzzy recombination operator; heterogeneous model; asynchronous migration

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Linear Evolutionary Algorithm

Kezong Tang, Xiaojing Yuan, Puchen Liu et al. · InTech eBooks · 2011

During the past three decades, global optimization problems (including single-objective optimization problems (SOP) and multi-objective optimization problems (MOP)) have been intensively studied not only in Computer Science, but also in Engineering. There are many solutions in literature, such as gradient projection method [1-3], Lagrangian and augmented Lagrangian penalty methods Among these methods, penalty function method is an important approach to solve global optimization problems.. To obtain the optimal solution of the original problem, the first step is to convert the optimization problem into an unconstrained optimization problem with a certain penalty function (such as Lagrangian multiplier). As the penalty multiplier approaches zero or infinite, the iteration point might approach optimal too. However, at the same time, the objective function of the unconstrained optimization problem might gradually become worse. This leads to increased computational complexity and long computational time in implementing the penalty function method to solve the complex optimization problems. In most of the research, both the original constraints and objective function are required to be smooth (or differentiable). However, in real-world problem, it is seldom to be able to guarantee a derivative for of the specific complex optimization problem. Hence, the development of efficient algorithms for handling complex optimization problems is of great importance. In this chapter, we present a new framework and algorithm that can solve problems belong to the family of stochastic search algorithms, often referred to as evolutionary algorithms. Evolutionary algorithms (EAs) are stochastic optimization techniques based on natural evolution and survival of the fittest strategy found in biological organisms. Evolutionary algorithms have been successfully applied to solve complex optimization problems in business Some commonly used EAs are Genetic algorithms (GAs) Each of these methods has its own characteristics, strengths and weaknesses. In general, a EA algorithm generate a set of initial solutions randomly based on the given seed and population size. Afterwards, it will go through evolution operations such as cross-over and mutation before evaluated by the www.intechopen.com

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The evolutionary algorithm.

A. Bradley Duthie (11143851), Rosie Mangan (17550035), C. Rose McKeon (17550038) et al. · Figshare · 2023

<p>The evolutionary algorithm of the resevol R package, including key data structures used, a general overview of the evolutionary algorithm, and details concerning haploid and diploid individuals.</p> <p>(PDF)</p>

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Diffusion Evolutionary Algorithm

Mészáros, István · Brno University of Technology Digital Library (Brno University of Technology) · 2010

There are new trends in artificial intelligence nowadays. Methods known as evolutionary algorithms are one of them. These algorithms allow us to design and optimize systems using computers. One of the variants of evolutionary algorithms is the diffusion evolutionary algorithm. This type of algorithms is able to run in parallel, and besides that it brings many positive features. The question is under what conditions the diffusion variant of evolutionary algorithms can effectively be used. Is it possible to use for planning systems and for problem optimization? Why are they more favorable than other types of evolutionary algorithms? This work tries to answer these questions and explain the behavior of these algorithms.

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Cognition Evolutionary Algorithm

Yifan Zhu · 2010

Inspired by the process of human creative thinking,cognition evolutionary algorithm,a novel intelligent algorithm based on cognition science and computational creativity,was proposed,which simulates the creative thinking based problem solving process and behaviors.The algorithm is comprised of six components which are divergent thinking,convergent thinking,memory,execution,learning and value measures.Taking the problem-solving as a knowledge based creative thinking process,knowledge evolution and knowledge based creative thinking skills play important roles in the algorithm.The impact of the parameters of cognition evolutionary algorithm on the performance of the algorithm was analyzed through an extended path optimization problem.The results show that the novel algorithm can reduce object score evaluation times compared with other classic intelligent algorithms for solving knowledge intensive optimization problems.

Cited by 0

Keywords: Evolutionary algorithm

2014

Model tree the problem of decision tree induction. Recently, an EA was proposed to evolve model ks. The main dif-l node is re o be NP-co [24]. Consequently, practical decision-tree learning algorithms are based on heuristics such as greedy algorithms, locally optimal decisions are made in each tree node. Such algorithms cannot guarantee to return the globally optim sion tree. The purpose of this paper is to illustrate the application of a specialized evolutionary algorithm (EA) [27] to the problem of model tree induction. The objectives are to show that evolutionary induction may result in finding globally

Cited by 0

Evolutionary Algorithm Paper

Sean Mochocki · Data Archiving and Networked Services (DANS) · 2020

This is the test data which supports the results outlined in the

Cited by 0Open access

Diffusion Evolutionary Algorithm

Žundálek, Zbyněk · Brno University of Technology Digital Library (Brno University of Technology) · 2011

Tato bakalářská práce se zabývá paralelizací difuzních evolučních algoritmů pomocí knihovny OpenMP. Náplní teoretické části práce je stručný úvod do problematiky evolučních a genetických algoritmů následovaný popisem paralelní verze těchto algoritmů na systémech se sdílenou pamětí. Teoretická část je zakončena rozborem klíčových vlastností knihovny OpenMP. Praktická část podrobně popisuje dvě možné varianty implementace difuzního evolučního algoritmu - synchronní a asynchronní. V experimentální části je na problému N dam provedeno srovnání těchto dvou variant s důrazem na maximální dosažené zrychlení. Kvalita nalezeného řešení je dále zkoumána s ohledem na použitý typ okolí, topologie a operátoru nahrazení.

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Systemically Evolutionary Algorithm

Jerzy Tchorzewski, Emil Chyzy · 2016

Abstract. The paper contains the concept and the results of research concerning the evolutionary algorithm, identified based on the systems control theory, which was called the Systemically of Evolutionary Algorithm (SAE). Special attention was paid to two elements of evolutionary algorithms, which have not been fully solved yet, i.e. to the methods used to create the initial population and the method of creating the robustness (fitness) function. Other elements of the SEA algorithm, i.e. cross-over, mutation, selection, etc. were also defined from a systemic point of view. Computational experiments were conducted using a selected subsystem of the Polish Electrical Power System and three programming languages: Java, C++ and Matlab. Selected comparative results for the SAE algorithm in different implementations were also presented.

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Spatial evolutionary algorithm

Roman Krzanowski, Jonathan Raper · 1998

Evolutionary modelling methods have gained broad recognition by many researchers engaged in divergent scientific fields ( Davis, 1 99 1 ; Goldberg, 1 989; Holland, 1 993) . Indeed, several hundreds of applications of evolutionary based techniques bear witness to the cogency of this modelling approach ( Liepins and Hillard, 1 989; Michalewicz, 1 992; Mitchell, 1 996; Nissen, 1 993) .

Cited by 1

The Mixing Evolutionary Algorithm

C. H. M. Van Kemenade, Cees H. M. Van Kemenade · 1997

and their applications. SMC is sponsored by the Netherlands Organization for Scientific Research (NWO). CWI is a member of

Cited by 0

An evolutionary reincarnation algorithm

Prime, Ben, Hendtlass, Tim · Swinburne Research Bank (Swinburne University of Technology) · 2024

As there is little or no experimental experience of reincarnation in the natural world, attempts to add a reincarnation metaphor to an evolutionary algorithm must of necessity proceed cautiously. In previous work the authors have established that the reintroduction of previously stored gene values into the population can have a noticeable effect on the progress of evolution, this paper now considers a range of options for deciding which gene values to store, which to return and which individuals in the current population should receive the returned gene values. Consistent experimental results on three well known functions allow a suggestion to be made of, if not the best choices, at least a good choice selection to use on initial experiments on other problems.

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