Everything about Evolutionary algorithm
Subset of evolutionary computation
Evolutionary algorithms (EA) reproduce essential elements of biological evolution in a computer algorithm in order to solve "difficult" problems, at least approximately, for which no exact or satisfactory solution methods are known. They are metaheuristics and population-based bio-inspired algorithms and evolutionary computation, which itself are part of the field of computational intelligence. The mechanisms of biological evolution that an EA mainly imitates are reproduction, mutation, recombination and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the fitness function determines the quality of the solutions. Evolution of the population then takes place after the repeated application of the above operators. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Evolutionary algorithmA 5-minute orientation
- Skim a recent paper: Human Evolutionary Optimization AlgorithmInfluential work since 2022
- Pick up a classic book: Genetic and Evolutionary ComputingThe most-reprinted book on the subject
- Read the landmark paper: MOEA/D: A Multiobjective Evolutionary Algorithm Based on DecompositionCited 9,593 times
- Go sideways: Evolutionary computationA closely related topic
- Go sideways: Memetic algorithmA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints
A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms
🆕 Influential since 2022
Human Evolutionary Optimization Algorithm
A Self-Adaptive Evolutionary Multi-Task Based Constrained Multi-Objective Evolutionary Algorithm
Partial reinforcement optimizer: An evolutionary optimization algorithm
Evolutionary mating algorithm
Multifactorial Evolutionary Algorithm Based on Diffusion Gradient Descent
📚 Books worth reading
Genetic and Evolutionary ComputingJeng-Shyang Pan, Jerry Chun-Wei Lin · 2013
Genetic algorithms + data structures = evolution programsZbigniew Michalewicz · 1992
Artificial evolutionPierre Collet, Jin-Kao Hao · 2002
Swarm IntelligenceYing Tan · 2018
Genetic algorithms and genetic programmingMichael Affenzeller, Stefan Wagner · 2008- 📕Discrete Problems in Nature Inspired AlgorithmsAnupam Prof Shukla, Ritu Tiwari · 2017
Evolutionary Algorithms for Mobile Ad Hoc NetworksBernabé Dorronsoro, Patricia Ruiz · 2014
Evolutionary computationKenneth A. De Jong · 2001
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Evolutionary computation Evolutionary computation (EC) from computer science is a family of algorithms for global optimization inspired by biological evolution, and a subfield…
- Memetic algorithm In computer science and operations research, a memetic algorithm (MA) is an extension of an evolutionary algorithm (EA) that aims to accelerate the evolutionary…
- Genetic representation In computer programming, genetic representation is a way of presenting solutions/individuals in evolutionary computation methods. The term encompasses…
- Population model (evolutionary algorithm) The population model of an evolutionary algorithm (EA) describes the structural properties of its population to which its members are subject. A population…
- Premature convergence Premature convergence is an unwanted effect in evolutionary algorithms (EA), a metaheuristic that mimics the basic principles of biological evolution as…
- Metaheuristic In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, tune, or select…
- Chromosome (evolutionary algorithm) A chromosome or genotype in evolutionary algorithms (EA) is a set of parameters which define a proposed solution of the problem that the evolutionary algorithm…
- Fitness function A fitness function is a particular type of objective or cost function that is used to summarize, as a single figure of merit, how close a given candidate…