Evolutionary Strategy Algorithms
Pp. 84-113 (30)
Andre A. Keller
Evolutionary computation (EC) is commonly divided into evolution strategy
(ES), evolution programming (EP), genetic algorithm (GA), and genetic programming
(GP). This study focuses on GA algorithms for solving MOOP problems. GA belongs
to the set of nature-inspired meta-algorithms. GA’s stochastic search techniques are
based on genetic processes of biological systems. GA uses encodings and reproduction
mechanisms. A population of feasible solutions evolves over successive generations
with improved performances. This process stops when reaching global optima. The
reproduction uses three types of operators: a selection operator determines the most
qualified solutions (i.e., individuals), a crossover operator allows creating new
solutions, and a mutation operator makes one or more random changes in a single
string. In this study, we show how we can combine different modules with an early
version of Mathematica® software. Each module specifies input and output variables.
Other free packages SciLab 5.5.2 (an alternative to Matlab®) and GENOCOP III use
the genetic search algorithm solvers for SOOP and MOOP problems. Ackley’s test
function and other examples illustrate such computations.
Ackley’s type function, Biased roulette wheel, Crossover operator,
Encoding technique, Gene, Genetic algorithm, Genome, Genotype, GENOCOP
III package, Mathematica® notebook, Mathematica® primitives, Mating
population, Mutation operator, Offspring population, Reproduction mechanism,
SciLab 5.5.2 software, Selection operator.
Center for Research in Computer Science Signal and Automatic Control of Lille University of Lille – CNRS France.