EVO* 2019 - Late-Breaking Abstracts Volume

  title={EVO* 2019 - Late-Breaking Abstracts Volume},
  author={A. M. Mora and Anna I. Esparcia-Alc{\'a}zar},
This volume contains the Late-Breaking Abstracts submitted to the EVO* 2019 Conference, that took place in Leipzig, from 24 to 26 of April. These papers where presented as short talks and also at the poster session of the conference together with other regular submissions. All of them present ongoing research and preliminary results investigating on the application of different approaches of Evolutionary Computation to different problems, most of them real world ones. 

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On the Locality of Grammatical Evolution
The results show that the representation used in GE has problems with locality as many neighboring genotypes do not correspond to neighboring phenotypes, and it leads to lower performance for mutation-based search approaches in comparison to standard GP representations.
A Representation of Artificial Spin Ice for Evolutionary Search
Arrangements of nanomagnets known as artificial spin ices show great potential for use in unconventional computation. The majority of exploratory work done in this area considers just a small handful
Evolutionary Programming and Evolution Strategies: Similarities and Differences
Theoretical results on global convergence step size control for a strictly convex quadratic function and an extension of the convergence rate the ory for Evolution Strategies are presented and discussed with respect to their implications on Evolutionary Pro gramming.
Grammar-based Genetic Programming: a survey
This work surveys the various grammar-based formalisms that have been used in GP and discusses the contributions they have made to the progress of GP, showing how grammar formalisms contributed to the solutions of these problems.
The Gn,m Phase Transition is Not Hard for the Hamiltonian Cycle Problem
Using an improved backtrack algorithm with sophisticated pruning techniques, we revise previous observations correlating a high frequency of hard to solve Hamiltonian cycle instances with the Gn,m
Structured Grammatical Evolution: A Dynamic Approach
Dynamic Structured Grammatical Evolution (DSGE) is introduced, which introduces a one-to-one mapping between the genotype and the non-terminals and shows that DSGE performance is never worse than SGE, being statistically superior in a considerable number of the tested problems.
A Comparative Study of Different Grammar-Based Genetic Programming Approaches
A comparative study between CFG-GP, GE and SGE to examine their relative performance is performed, showing that SGE is a good alternative to GE.
A Predictive Data Analytic for the Hardness of Hamiltonian Cycle Problem Instances
This study duplicates the original experiment and extends it with two more algorithms, concluding that the order parameter based on problem instance data analytics is useful across different algorithms.
A Survey on Parallel Genetic Algorithms for Shop Scheduling Problems
  • Jia-xiang LuoD. E. Baz
  • Business, Computer Science
    2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)
  • 2018
This paper presents the state of the art with respect to the recent works on solving shop scheduling problems using parallel GAs and analyzes their designs based on the frameworks.
Revisiting the 4-part harmonization problem with GAs: A critical review and proposals for improving
The four-part harmonization problem is a well known problem that has been studied in the last three centuries by music scholars. The goal is to build up three different voices, melodies, based on a