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Everything about Crossover (evolutionary algorithm)
Operator used to vary the programming of chromosomes from one generation to the next
Crossover in evolutionary algorithms and evolutionary computation, also called recombination, is a genetic operator used to combine the genetic information of two parents to generate new offspring. It is one way to stochastically generate new solutions from an existing population, and is analogous to the crossover that happens during sexual reproduction in biology. New solutions can also be generated by cloning an existing solution, which is analogous to asexual reproduction. Newly generated solutions may be mutated before being added to the population. The aim of recombination is to transfer good characteristics from two different parents to one child. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Crossover (evolutionary algorithm)A 5-minute orientation
- Skim a recent paper: A multi-objective evolutionary algorithm with interval based initialization and self-adaptive crossover operator for large-scale feature selection in classificationInfluential work since 2022
- Pick up a classic book: Genetic ProgrammingThe most-reprinted book on the subject
- Read the landmark paper: A novel adaptive hybrid crossover operator for multiobjective evolutionary algorithmCited 59 times
- Go sideways: Genetic operatorA closely related topic
- Go sideways: Mutation (evolutionary algorithm)A closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
A novel adaptive hybrid crossover operator for multiobjective evolutionary algorithm
A Simplex Crossover based evolutionary algorithm including the genetic diversity as objective
MOEA/D-ARA+SBX: A new multi-objective evolutionary algorithm based on decomposition with artificial raindrop algorithm and simulated binary crossover
An Evolutionary Multiobjective Carpool Algorithm Using Set-Based Operator Based on Simulated Binary Crossover
IBDA: Improved Binary Dragonfly Algorithm With Evolutionary Population Dynamics and Adaptive Crossover for Feature Selection
A+ Evolutionary search algorithm and QR decomposition based rotation invariant crossover operator
Best-order crossover for permutation-based evolutionary algorithms
Artificial neural network based crossover for evolutionary algorithms
🆕 Influential since 2022
A multi-objective evolutionary algorithm with interval based initialization and self-adaptive crossover operator for large-scale feature selection in classification
An Improved Strength Pareto Evolutionary Algorithm 2 with Adaptive Crossover Operator for Bi-Objective Distributed Unmanned Aerial Vehicle Delivery
Wasserstein crossover for evolutionary algorithm-based topology optimization
Crossover Operator Inspired by the Selection Operator for an Evolutionary Task Sequencing Algorithm
Slime mould algorithm with horizontal crossover and adaptive evolutionary strategy: performance design for engineering problems
📚 Books worth reading
Genetic ProgrammingKrzysztof Krawiec · 2013
Evolutionary Computation, Machine Learning and Data Mining in BioinformaticsLeonardo Vanneschi · 2012
Self-adaptive heuristics for evolutionary computationOliver Kramer · 2008
Evolutionary Computation in Combinatorial OptimizationMartin Middendorf · 2013
Foundations of genetic algorithms 7Kenneth A. De Jong, Riccardo Poli · 2001
MetaheuristicsMauricio G. C. Resende, Jorge Pinho de Sousa · 2004
Advances in Neural Networks – ISNN 2013Chengan Guo · 2013
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
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