Open AccessBook
Evolutionary algorithms in theory and practice
TLDR
In this work, the author compares the three most prominent representatives of evolutionary algorithms: genetic algorithms, evolution strategies, and evolutionary programming within a unified framework, thereby clarifying the similarities and differences of these methods.About:
The article was published on 1996-01-01 and is currently open access. It has received 2679 citations till now. The article focuses on the topics: Evolutionary music & Evolutionary programming.read more
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Evolutionary algorithms + domain knowledge = real-world evolutionary computation
TL;DR: The implicit and explicit knowledge representation mechanisms for evolutionary algorithms (EAs) are discussed and offline and online metaheuristics as examples of explicit methods to leverage this knowledge are described.
Book
Noisy Optimization With Evolution Strategies
TL;DR: This paper aims to provide a Discussion of the Overvaluation of Sampling and Selection in relation to Distributed Populations and its Applications in the context of Genetic Repair.
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A comprehensive review of firefly algorithms
TL;DR: A comprehensive review of this living and evolving discipline of Swarm Intelligence shows that the firefly algorithm could be applied to every problem arising in practice and encourages new researchers and algorithm developers to use this simple and yet very efficient algorithm for problem solving.
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Joint redundancy and maintenance optimization for multistate series–parallel systems
TL;DR: In this article, the joint redundancy and replacement schedule optimization problem is generalized to multistate system, where the system and its components have a range of performance levels and each element is characterized by its capacity, reliability and cost.
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Balancing exploration and exploitation with adaptive variation for evolutionary multi-objective optimization
TL;DR: An adaptive variation operator is proposed that exploits the chromosomal structure of binary representation and synergizes the function of crossover and mutation and ensures an efficient exchange of information between the different chromosomal sub-structures throughout the evolutionary search.