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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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Investigating the Effect of Imbalance Between Convergence and Diversity in Evolutionary Multiobjective Algorithms
TL;DR: This paper characterizes an imbalanced MOP by clearly defining properties and indicating the reasons for the existing EMO algorithms’ difficulties in solving them, and presents 14 imbalanced problems, with and without constraints.
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Evolutionary approaches to the design and organization of manufacturing systems
TL;DR: In this paper, the main characteristics of evolutionary algorithms and their potential to handle features frequently encountered in manufacturing problems (such as multiple criteria and constraints), each type of problem identified is studied.
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Scalability and efficiency in multi-relational data mining
Hendrik Blockeel,Michèle Sebag +1 more
TL;DR: This article attempts to present a structured overview of efficiency and Scalability of multi-relational data mining approaches through a number of theoretical results, algorithms and implementations.
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Evolving pattern recognition systems
TL;DR: A hybrid evolutionary learning algorithm is presented that synthesizes a complete multiclass pattern recognition system that blends elements of evolutionary programming, genetic programming, and genetic algorithms to perform a search for an effective set of feature detectors.
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Assessment of the CO2 emission and cost reduction performance of a low-carbon-emission concrete mix design using an optimal mix design system
TL;DR: In this paper, the authors evaluated the appropriateness and the reduction performance of the low-carbon-emission concrete (LCEC) mix design system and the deduced mix design results using an evolutionary algorithm (EA), the optimal mix design method, which minimizes the CO2 emission of the concrete mix design.