13th International Conference, Évolution Artificielle, EA 2017, Paris, France, October 25-27, 2017, Revised selected papers /
First Statement of Responsibility
Evelyne Lutton, Pierrick Legrand, Pierre Parrend, Nicolas Monmarché, Marc Schoenauer (eds.).
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
Cham, Switzerland :
Name of Publisher, Distributor, etc.
Springer,
Date of Publication, Distribution, etc.
2018.
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
1 online resource (xvi, 231 pages) :
Other Physical Details
illustrations
SERIES
Series Title
Lecture notes in computer science,
Series Title
LNCS sublibrary. SL 1, Theoretical computer science and general issues
Volume Designation
10764
ISSN of Series
0302-9743 ;
GENERAL NOTES
Text of Note
Includes author index.
CONTENTS NOTE
Text of Note
Intro -- Preface -- Évolution Artificielle 2017 -- EA 2017 -- Abstracts of Invited Talks -- The Cartography of Computational Search Spaces -- Progressive Data Analysis: A New Computation Paradigm for Scalability in Exploratory Data Analysis -- Contents -- On the Design of a Master-Worker Adaptive Algorithm Selection Framework -- 1 Introduction -- 2 Related Works -- 2.1 Sequential Adaptive Algorithm Selection -- 2.2 Parallel Adaptive Algorithm Selection -- 2.3 Benchmarks: The Fitness Cloud Model -- 3 M/W Framework Description -- 3.1 Aggregation of Local Reward Values
Text of Note
3.2 Homogeneous vs. Heterogeneous Adaptive Selection -- 4 Experimental Analysis -- 4.1 Overall Relative Performance -- 4.2 Analysis of the Reward Aggregation Functions -- 4.3 Analysis of the Heterogeneity Scenarios -- 5 Conclusions -- References -- Comparison of Acceptance Criteria in Randomized Local Searches -- 1 Introduction -- 2 Literature Review -- 3 Experimental Setup -- 4 Experiments on the Quadratic Assignment Problem -- 5 Experiments on the Permutation Flow-Shop Problem -- 6 Conclusions -- References
Text of Note
3.2 Semantic Crossover for Program Synthesis -- 4 Experimental Setup -- 4.1 Benchmark Problems -- 5 Results -- 5.1 Successful Runs and Fitness -- 5.2 Parent Comparison -- 5.3 Types Selected for Similarity Measurement -- 6 Conclusion and Future Work -- References -- On the Use of Dynamic GP Fitness Cases in Static and Dynamic Optimisation Problems -- 1 Introduction -- 2 Related Work -- 2.1 Fitness Cases in Genetic Programming -- 2.2 Promoting and Maintaining Diversity -- 3 Proposed Approaches -- 3.1 Dynamic Fitness Cases -- 3.2 Kendall Tau Distance -- 4 Experimental Setup
Text of Note
A Fitness Landscape View on the Tuning of an Asynchronous Master-Worker EA for Nuclear Reactor Design -- 1 Introduction -- 2 Preliminaries -- 2.1 Evolutionary Optimization for Nuclear Energy Problems -- 2.2 Parallel Evolutionary Algorithms -- 2.3 Landscape Aware Parameter Tuning -- 3 Problem Definition -- 3.1 Description of the System -- 3.2 Criterion of Interest -- 4 Asynchronous Parallel EA -- 4.1 Algorithm Definition -- 4.2 Mutation Operator -- 5 Experimental Analysis -- 5.1 Baseline Parameters Setting -- 5.2 Impact of the Mutation Parameters -- 5.3 Fitness Landscape Analysis -- 6 Conclusions
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SUMMARY OR ABSTRACT
Text of Note
This book constitutes the thoroughly refereed post-conference proceedings of the 13th International Conference on Artificial Evolution, EA 2017, held in Paris, France, in October 2017. The 16 revised papers were carefully reviewed and selected from 33 submissions. The papers cover a wide range of topics in the field of artificial evolution, such as evolutionary computation, evolutionary optimization, co-evolution, artificial life, population dynamics, theory, algorithmics and modeling, implementations, application of evolutionary paradigms to the real world (industry, biosciences ...), other biologically-inspired paradigms (swarm, artificial ants, artificial immune systems, cultural algorithms ...), memetic algorithms, multi-objective optimisation, constraint handling, parallel algorithms, dynamic optimization, machine learning and hybridization with other soft computing techniques.