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New Achievements in Evolutionary Computation - Bog

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Evolutionary computation has been widely used in computer science for decades. Eventhough it started as far back as the 1960s with simulated evolution, the subject is stillevolving. During this time, new metaheuristic optimization approaches, like evolutionaryalgorithms, genetic algorithms, swarm intelligence, etc., were being developed and newfields of usage in artificial intelligence, machine learning, combinatorial and numericaloptimization, etc., were being explored. However, even with so much work done, novelresearch into new techniques and new areas of usage is far from over. This book presentssome new theoretical as well as practical aspects of evolutionary computation. This book will be of great value to undergraduates, graduate students, researchers incomputer science, and anyone else with an interest in learning about the latestdevelopments in evolutionary computation.

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9789533070537
  • Indbinding:
  • Hardback
  • Sideantal:
  • 330
  • Udgivet:
  • 1. februar 2010
  • Størrelse:
  • 244x170x19 mm.
  • Vægt:
  • 726 g.
  • 2-3 uger.
  • 22. januar 2025

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Beskrivelse af New Achievements in Evolutionary Computation

Evolutionary computation has been widely used in computer science for decades. Eventhough it started as far back as the 1960s with simulated evolution, the subject is stillevolving. During this time, new metaheuristic optimization approaches, like evolutionaryalgorithms, genetic algorithms, swarm intelligence, etc., were being developed and newfields of usage in artificial intelligence, machine learning, combinatorial and numericaloptimization, etc., were being explored. However, even with so much work done, novelresearch into new techniques and new areas of usage is far from over. This book presentssome new theoretical as well as practical aspects of evolutionary computation. This book will be of great value to undergraduates, graduate students, researchers incomputer science, and anyone else with an interest in learning about the latestdevelopments in evolutionary computation.

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