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Everything about Evolutionary programming
Evolutionary algorithm with a defined structure
Evolutionary programming is an evolutionary algorithm, where a share of the new population is created by mutating the previous population without crossover. Evolutionary programming differs from evolution strategy ES in one detail. All individuals are selected for the new population, while in ES, every individual has the same probability to be selected. It is one of the four major evolutionary algorithm paradigms. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Evolutionary programmingA 5-minute orientation
- Skim a recent paper: Fifty years of multi-objective optimization and decision-making: From mathematical programming to evolutionary computationInfluential work since 2022
- Pick up a classic book: Genetic and Evolutionary ComputingThe most-reprinted book on the subject
- Read the landmark paper: Evolutionary programming made fasterCited 3,344 times
- Go sideways: Evolutionary computationA closely related topic
- Go sideways: Evolutionary algorithmA closely related topic
📖 Definitions
Noun A population-based, stochastic search and optimization paradigm within evolutionary computation in which candidate solutions undergo mutation and fitness-based selection over successive generations.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Evolutionary programming made faster
Evolutionary programming based optimal power flow algorithm
Optimal reactive power planning using evolutionary algorithms: a comparative study for evolutionary programming, evolutionary strategy, genetic algorithm, and linear programming
Combining mutation operators in evolutionary programming
Optimal Power Flow by Improved Evolutionary Programming
Evolutionary Programming in Electromagnetic Optimization: A Review
Ensemble strategies with adaptive evolutionary programming
Evolving Evolutionary Algorithms Using Linear Genetic Programming
🆕 Influential since 2022
Fifty years of multi-objective optimization and decision-making: From mathematical programming to evolutionary computation
Optimum Mobile Robot Path Planning Using Improved Artificial Bee Colony Algorithm and Evolutionary Programming
Genetic Programming-Based Evolutionary Deep Learning for Data-Efficient Image Classification
Optimizing Dynamic Flexible Job Shop Scheduling Using an Evolutionary Multitask Optimization Framework and Genetic Programming
Sustainable fertilizer supply chain network design using evolutionary-based resilient robust stochastic programming
📚 Books worth reading
Genetic and Evolutionary ComputingJeng-Shyang Pan, Jerry Chun-Wei Lin · 2013
Computational IntelligenceAndries P. Engelbrecht · 2002
Recent Advances in Natural ComputingYasuhiro Suzuki, Masami Hagiya · 2014
Artificial evolutionPierre Collet, Jin-Kao Hao · 2002
Evolutionary computationDavid B. Fogel, Zbigniew Michalewicz · 1998
Evolutionary computationKenneth A. De Jong · 2001- 📕Genetic and Evolutionary ComputationStefano Cagnoni, Stephen L. Smith · 2010
Exploitation of linkage learning in evolutionary algorithmsYing-ping Chen · 2010
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…
- Evolutionary algorithm Evolutionary algorithms (EA) reproduce essential elements of biological evolution in a computer algorithm in order to solve "difficult" problems, at least…
- Table of metaheuristics This is a chronological table of metaheuristic algorithms that only contains fundamental computational intelligence algorithms. Hybrid algorithms and multi-obje…
- Metaheuristic In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, tune, or select…
- Genetic programming Genetic programming (GP) is an evolutionary algorithm, an artificial intelligence technique mimicking natural evolution, which operates on a population…
- Evolution strategy In computer science, evolution strategy (ES) is a subclass of evolutionary algorithms, which serves as an optimization technique. It uses the major genetic…
- Mutation (evolutionary algorithm) Mutation is a genetic operator used to maintain genetic diversity of the chromosomes of a population of an evolutionary algorithm (EA), including genetic…
- Meta-optimization Meta-optimization from numerical optimization is the use of one optimization method to tune another optimization method. Meta-optimization is reported…