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About 10,000 papers for “Evolutionary Algorithms and Applications”

PARALLEL EVOLUTIONARY ALGORITHMS AND APPLICATIONS

Lishan Kang, Yuping Chen · 2008

research center on parallel algorithms in China. Recently emphasis research areas in parallel problem solvers are 1) Evolutionary algorithms based on imitating the evolution processes of living

Cited by 0

Evolutionary Algorithms: Theory and Applications

Heinz Mühlenbein · 1993

Evolutionary algorithms which model natural evolution processes have been successfully used for optimization. Theoretical explanations why and how the algorithms work have been less successful. In this paper evolutionary algorithms are considered as random search methods. The genetic operators mutation and recombination are evaluated according to the measure expected progress. Mutation is analyzed by Markov chains and probability theory. Recombination is investigated together with selection by methods of quantitative genetics. Furthermore it is shown that hillclimbing improves the efficiency of the search in ruggged landscapes. The theoretical results are used in two evolutionary algorithms - the parallel genetic algorithm PGA and the breeder genetic algorithm BGA. The PGA models natural evolution which self-organizes itself, the BGA models rational controlled evolution by a virtual breeder. The efficiency of both algorithms is shown with numerical examples. Results of a real world pro...

Cited by 0

Evolutionary Algorithms: Principles and Applications

Ivan Zelinka · Wiley Encyclopedia of Electrical and Electronics Engineering · 2015

Evolutionary algorithms (EAs) are search methods that can be used for solving optimization problems. They mimic principles of natural evolution by employing a population‐based approach (or swarm‐like), associating each individual of the population with the so‐called fitness and including elements of randomness, although the random is directed through a selection process. This article presents the basic principles of evolutionary algorithms and discusses their dynamics, structure, and applications. In doing so, it is particularly shown how the fundamental understanding of natural evolution processes has cleared the ground for the origin of evolutionary algorithms. Major implementation variants and their structural as well as functional elements are discussed here with attention on examples from electrical engineering.

Cited by 2

Evolutionary Algorithms, Multiobjective Optimization, and Applications

Eckart Zitzler · 2003

This paper gives an introduction into evolutionary computation, in particular in the light of multiobjective optimization, and demonstrates how evolutionary algorithms can be used to tackle a highly demanding application in telecommunications, namely the design of a network processor.

Cited by 0

Mathematical Optimization and Evolutionary Algorithms with Applications

Ponsich, Antonin, Vila Bonilla, Mariona, Domenech, Bruno · 2023

Optimization is present almost everywhere in real life, resulting in a wide spectrum of scientific and engineering areas with applications that can be formalized as optimization problems. This feature has fostered the development of research studies aiming to design and implement efficient optimization methods able to address the increasing complexity of applications that are intended to be solved. These studies are typically divided into two areas: one focuses on the theoretical development of advanced solution strategies through the perspective of tackling problems of increasing complexity; another toward developing problem-devoted techniques that aim to efficiently find high-quality solutions to specific applications drawn from a wide spectrum of areas (engineering, social sciences, biotechnologies, finances, etc.). The articles included in this reprint illustrate both types of studies. The reprint is a collection of all the articles accepted and published in the Special Issue titled "Mathematical Optimization and Evolutionary Algorithms with Applications of the journal Mathematics. We hope that readers will benefit from the insights provided by these papers and contribute to the fast-paced growth of these areas. We also hope that the resulting mixture of methods, algorithms and applications for the treatment of complex optimization problems presented in this Special Issue, either through mathematical tools or metaheuristic algorithms, contributes to the development of research in this area.

Cited by 0Open access

NOVEL EVOLUTIONARY GLOBAL OPTIMIZATION ALGORITHMS AND THEIR APPLICATIONS

Wang, Hualin, Ersoy, Okan · Purdue e-Pubs (Purdue University System) · 2006

The gray code optimization (GCO) algorithm is a deterministic global optimization algorithm based on integer representation. It utilizes the adjacency property of Gray code representation. By controlling the number of bits flipped, it searches through the space effciently. A further development of the GCO algorithm is conducted in this research to avoid getting stuck in local minima. To further improve the performance, and take the advantage of cheaper but more powerful CPUs, a parallel computation paradigm using MPI is implemented. Analysis of the mechanism of the GCO algorithm indicated that it can be modeled by mixture gaussian. This led to a new stochastic evolutionary global optimization algorithm based on mixture of gaussians and real numbers. The EM algorithm is used to acquire the parameters of each Gaussian component. With a mathematic model in hand, a lot of theoretical questions, such as convergence property, convergence rate, and the benefits of using the mixture model could be investigated. The relationship between the proposed algorithms and other evolutionary algorithms including genetic algorithms, evolutionary programming and evolutionary strategy will be studied. To combine the merits of different evolutionary algorithms, a uniform global optimizer based on parallel computing was proposed to solve a broad range of problems. The proposed algorithms are general global optimization methods. They have a broad range of applications in engineering and science. The applications in molecular conformation search, curve fitting, and spectral analysis are reported in this report.

Cited by 0Open access

Evolutionary algorithms and their applications to engineering problems

Adam Słowik, Halina Kwaśnicka · Neural Computing and Applications · 2020

Abstract The main focus of this paper is on the family of evolutionary algorithms and their real-life applications. We present the following algorithms: genetic algorithms, genetic programming, differential evolution, evolution strategies, and evolutionary programming. Each technique is presented in the pseudo-code form, which can be used for its easy implementation in any programming language. We present the main properties of each algorithm described in this paper. We also show many state-of-the-art practical applications and modifications of the early evolutionary methods. The open research issues are indicated for the family of evolutionary algorithms.

Cited by 659Open access