13th International Conference, Évolution Artificielle, EA 2017, Paris, France, October 25-27, 2017, Revised selected papers /
Evelyne Lutton, Pierrick Legrand, Pierre Parrend, Nicolas Monmarché, Marc Schoenauer (eds.).
Cham, Switzerland :
Springer,
2018.
1 online resource (xvi, 231 pages) :
illustrations
Lecture notes in computer science,
LNCS sublibrary. SL 1, Theoretical computer science and general issues
10764
0302-9743 ;
Includes author index.
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
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
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
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
0
8
8
8
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.