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Evolutionary Computation 2 : Advanced Algorithms and Operators

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TLDR
The focus is on fitness evaluation, constraint-handling techniques, population structures, advanced techniques in evolutionary computation, and the implementation of evolutionary algorithms.
Abstract
Evolutionary Computation 2: Advanced Algorithms and Operators expands upon the basic ideas underlying evolutionary algorithms. The focus is on fitness evaluation, constraint-handling techniques, population structures, advanced techniques in evolutionary computation, and the implementation of evolutionary algorithms. It is intended to be used by individual researchers and students in the expanding field of evolutionary computation.

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Book

Evolutionary algorithms for solving multi-objective problems

TL;DR: This paper presents a meta-anatomy of the multi-Criteria Decision Making process, which aims to provide a scaffolding for the future development of multi-criteria decision-making systems.
Journal ArticleDOI

Learning and optimization using the clonal selection principle

TL;DR: This paper proposes a computational implementation of the clonal selection principle that explicitly takes into account the affinity maturation of the immune response and derives two versions of the algorithm, derived primarily to perform machine learning and pattern recognition tasks.
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GA: A Package for Genetic Algorithms in R

TL;DR: The R package GA is described, a collection of general purpose functions that provide a flexible set of tools for applying a wide range of genetic algorithm methods, ranging from mathematical functions in one and two dimensions known to be hard to optimize with standard derivative-based methods, to some selected statistical problems which require the optimization of user defined objective functions.
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Accelerating Differential Evolution Using an Adaptive Local Search

TL;DR: It is shown that the proposed new version of DE, with the adaptive LS, performs better, or at least comparably, to classic DE algorithm.
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Artificial immune systems as a novel soft computing paradigm

TL;DR: This paper proposes one such framework for AIS, discusses the suitability of AIS as a novel soft computing paradigm and reviews those works from the literature that integrate AIS with other approaches, focusing ANN, EA and FS.
References
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Journal ArticleDOI

Simulation of Genetic Systems by Automatic Digital Computers I. Introduction

TL;DR: Methods of setting automatic digital computers to simulate the algebraic aspects of reproduction, segregation, and selection and the application of these methods to the problem of the importance of linkage in multifactorial inheritance is illustrated by results from the SILLIAC.