EDDA-V2: an improvement of the evolutionary demes despeciation algorithm

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For any population-based algorithm, the initialization of the population is a very important step. In Genetic Programming (GP), in particular, initialization is known to play a crucial role - traditionally, a wide variety of trees of various sizes and shapes are desirable. In this paper, we propose an advancement of a previously conceived Evolutionary Demes Despeciation Algorithm (EDDA), inspired by the biological phenomenon of demes despeciation. In the pioneer design of EDDA, the initial population is generated using the best individuals obtained from a set of independent subpopulations (demes), which are evolved for a few generations, by means of conceptually different evolutionary algorithms - some use standard syntax-based GP and others use a semantics-based GP system. The new technique we propose here (EDDA-V2), imposes more diverse evolutionary conditions - each deme evolves using a distinct random sample of training data instances and input features. Experimental results show that EDDA-V2 is a feasible initialization technique: populations converge towards solutions with comparable or even better generalization ability with respect to the ones initialized with EDDA, by using significantly reduced computational time.

Original languageEnglish
Title of host publicationParallel Problem Solving from Nature – PPSN XV
Subtitle of host publication15th International Conference, 2018, Proceedings
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783319992525
Publication statusPublished - 1 Jan 2018
Event15th International Conference on Parallel Problem Solving from Nature, PPSN 2018 - Coimbra, Portugal
Duration: 8 Sep 201812 Sep 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11101 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th International Conference on Parallel Problem Solving from Nature, PPSN 2018


  • Despeciation
  • Initialization algorithm
  • Semantics


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