Graph structure optimization of Genetic Network Programming with ant colony mechanism in deterministic and stochastic environments
Created by W.Langdon from
gp-bibliography.bib Revision:1.8157
- @Article{ROSHANZAMIR:2019:swarm,
-
author = "Mohamad Roshanzamir and Maziar Palhang and
Abdolreza Mirzaei",
-
title = "Graph structure optimization of Genetic Network
Programming with ant colony mechanism in deterministic
and stochastic environments",
-
journal = "Swarm and Evolutionary Computation",
-
volume = "51",
-
pages = "100581",
-
year = "2019",
-
ISSN = "2210-6502",
-
DOI = "doi:10.1016/j.swevo.2019.100581",
-
URL = "http://www.sciencedirect.com/science/article/pii/S221065021831068X",
-
keywords = "genetic algorithms, genetic programming, Genetic
network programming, Ant colony algorithm, Agent
control problems, Deterministic environment, Stochastic
environment",
-
abstract = "Evolutionary Algorithms are of the most successful
algorithms in solving various optimization problems.
Genetic network programming is one of the Evolutionary
Algorithms with good capabilities in agent control
problems. In this algorithm, the individuals' structure
is a directed graph. Using this structure, it is
possible to model the solution of many complex
problems. However, in this algorithm, crossover and
mutation operators repeatedly break the structures of
individuals and make new ones. Although this can lead
to better structures, it may break suitable structures
in elite individuals. Meanwhile, in stochastic
environments, each time an individual is evaluated, it
leads to different fitness values. So, calculating the
fitness value of individuals requires evaluating each
individual several times. This extremely decreases the
evolution process speed. In this paper, inspired by
mechanisms of ant colony algorithm, a new method is
proposed to prevent the algorithm from iteratively
breaking down the structures of individuals. This
method improves the performance of individuals from one
generation to the next using a constructive process.
Unlike generative process that the individuals are
generated by combination of some others, in
constructive process they are produced according to the
experience of previous generations. Using this
mechanism, we not only prevent breaking suitable
structures but also can manage uncertainty in
stochastic environments. Our proposed method is used to
solve two agent control problems when the environment
is deterministic or stochastic. The results show that
the proposed algorithm has very high ability in
creating an efficient decision making strategies
especially in stochastic environments",
- }
Genetic Programming entries for
Mohamad Roshanzamir
Maziar Palhang
Abdolreza Mirzaei
Citations