Do not choose representation just change: an experimental study in states based EA

ABSTRACT

Our aim in this paper is to analyse the evolvability of diverse coding conversion operators in an instance of the states based evolutionary algorithm (SEA). Since the representation of solutions or the selection of the best encoding during the optimization process has been proved to be very important for the efficiency of evolutionary algorithms (EAs), we will discuss a strategy of coupling more than one representation and different procedures of conversion from one coding to another during the search. Elsewhere, some EAs try to use multiple representations (SM-GA, SEA, etc.) in intention to benefit from the characteristics of each of them. In spite of those results, this paper shows that the change of the representation is also a crucial approach to take into consideration while attempting to increase the performances of such EAs. As a demonstrative example, we use a two states SEA (2-SEA) which has two identical search spaces but different coding conversion operators. The results show that the way of changing from one coding to another and not only the choice of the best representation nor the representation itself is very advantageous and must be taken into account in order to well-desing and improve EAs execution.

REFERENCES

• 1: Franz Rothlauf , David E. Goldberg, Redundant representations in evolutionary computation, Evolutionary Computation, v.11 n.4, p.381-415, Winter 2003 [doi>10.1162/106365603322519288]
• 2: Maroun Bercachi, Philippe Collard, Manuel Clergue and Sebastien Verel "Evolving Dynamic Change and Exchange of Genotype Encoding in Genetic Algorithms for Difficult Optimization Problems". In Proceedings of CEC-07, (2007).
• 3: Sebastien Verel. "States based Evolutionary Algorithm". Technical Report, I3S Laboratory, (2008).
• 4: Philippe Collard and Manuel Clergue "Misleading Functions Designed from Alternation". In Proceedings of CEC-00, (2000).

INDEX TERMS

Primary Classification:
  G. Mathematics of Computing
  G.1 NUMERICAL ANALYSIS
      G.1.6 Optimization
          Subjects: Stochastic programming

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.8 Problem Solving, Control Methods, and Search
          Subjects: Heuristic methods

General Terms:
Algorithms, Experimentation, Performance

Keywords:
coding conversion, coding coupling, optimization, performance, representation, states based evolutionary algorithm