Evolutionary Algorithms (original) (raw)

Evolutionary optimization

2012

The emergence of different metaheuristics and their new variants in recent years has made the definition of the term Evolutionary Algorithms

A Study on Evolutionary Algorithms and Its Applications

Electrical and Automation Engineering, 2022

Evolutionary methods are a horror-based approach to solving problems that are not easily solved in polynomial time, for example, classical NP-heart problems and take longer to complete. Evolutionary methods are commonly used to provide good approximate solutions to problems that cannot be easily solved using other techniques. Many optimization issues fall into this category. It can be very calculated-finding a suitable solution is serious but sometimes the optimal solution is enough. Major classes of contemporaries (in the order of popularity) E.A. Genetic algorithms (GAs), evolutionary strategies (ESs), differential evolution (DE) and distribution algorithm evaluation (EDAs) are used. Evolutionary methods are based on the concepts of biological evolution. The 'population' of possible solutions to the problem will be created first, and each solution will be evaluated using a 'fitness function'. The population develops over time and (hopefully) identifies the best solutions.

Evolutionary Algorithms: What, When, and How?

Genetic algorithms (GAs) are a class of heuristic search methods based on natural selection. These algorithms find application in NP-Hard optimizations problems that do not solve efficiently using strictly analytic methods. The past decade saw an overwhelming increase in GA application in the real-world. However, many practitioners apply these algorithms hastily using software packages such as MATLAB, without even justifying the use of GAs. Actually, an effective implementation relies on and should reflect the user's understanding of the optimization problem and awareness of the limitations of algorithm: GAs are not the best alternative for all optimization problems, and the user has to integrate problem specific information in the algorithm. In this paper we highlight merits and demerits of GAs and discuss issues of implementation to practical problems.

Genetic Algorithm Based on Natural Selection Theory for Optimization Problems

Symmetry

The metaheuristic genetic algorithm (GA) is based on the natural selection process that falls under the umbrella category of evolutionary algorithms (EA). Genetic algorithms are typically utilized for generating high-quality solutions for search and optimization problems by depending on bio-oriented operators such as selection, crossover, and mutation. However, the GA still suffers from some downsides and needs to be improved so as to attain greater control of exploitation and exploration concerning creating a new population and randomness involvement happening in the population at the solution initialization. Furthermore, the mutation is imposed upon the new chromosomes and hence prevents the achievement of an optimal solution. Therefore, this study presents a new GA that is centered on the natural selection theory and it aims to improve the control of exploitation and exploration. The proposed algorithm is called genetic algorithm based on natural selection theory (GABONST). Two a...

On the equivalences and differences of evolutionary algorithms

Engineering Applications of Artificial Intelligence, 2013

Evolutionary algorithms (EAs) are fast and robust computation methods for global optimization, and have been widely used in many real-world applications. We first conceptually discuss the equivalences of various popular EAs including genetic algorithm (GA), biogeography-based optimization (BBO), differential evolution (DE), evolution strategy (ES) and particle swarm optimization (PSO). We find that the basic versions of BBO, DE, ES and PSO are equal to the GA with global uniform recombination (GA/GUR) under certain conditions. Then we discuss their differences based on biological motivations and implementation details, and point out that their distinctions enhance the diversity of EA research and applications. To further study the characteristics of various EAs, we compare the basic versions and advanced versions of GA, BBO, DE, ES and PSO to explore their optimization ability on a set of real-world continuous optimization problems. Empirical results show that among the basic versions of the algorithms, BBO performs best on the benchmarks that we studied. Among the advanced versions of the algorithms, DE and ES perform best on the benchmarks that we studied. However, our main conclusion is that the conceptual equivalence of the algorithms is supported by the fact that algorithmic modifications result in very different performance levels.

On Some Basic Concepts of Genetic Algorithms as A Meta-Heuristic Method for Solving of Optimization Problems

The genetic algorithms represent a family of algorithms using some of genetic principles being present in nature, in order to solve particular computational problems. These natural principles are: inheritance, crossover, mutation, survival of the fittest, migrations and so on. The paper describes the most important aspects of a genetic algorithm as a stochastic method for solving various classes of optimization problems. It also describes the basic genetic operator selection, crossover and mutation, serving for a new generation of individuals to achieve an optimal or a good enough solution of an optimization problem being in question.

Evolutionary algorithms and their applications to engineering problems

Neural Computing and Applications, 2020

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.