Authors:
Christopher Rajah
and
Nelishia Pillay
Affiliation:
University of KwaZulu-Natal, South Africa
Keyword(s):
Development, Evolution, Biologically-inspired Computing, Bin-packing.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Genetic Algorithms
;
Hybrid Systems
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Soft Computing
Abstract:
The literature highlights the effectiveness of emulating processes from nature to solve complex optimization
problems. Two processes in particular that have been investigated are evolution and development. Evolution
is achieved by genetic algorithms and the developmental approach was introduced to achieve development.
The developmental approach differs from other metaheuristics in that it does not explore the search space
applying intensification and diversification to a complete candidate solution. Instead intensification and
diversification are performed incrementally, at each step in the process of creating a solution. This is based
on an analogy from nature in which a multicellular organism is created incrementally rather than firstly being
completely developed and then improved to be fitter. Evolution on the other hand is used to explore the space
by applying intensification and diversification to randomly created candidate solutions with the aim of
improving the fitnes
s of these candidate solutions and ultimately producing a solution to the problem. Given
that in nature once an organism is initially developed its development or growth does not stop at that point
but certain cells may continue to grow until a certain point in an organism’s life span, it was felt that the
developmental approach terminated prematurely. It was hypothesized that a combination of both these
processes, instantiated with development and followed by evolution, would better emulate the processes in
nature and would be more effective at exploring the search space. The objective of the research presented in
the paper is to test this hypothesis. In terms of search this would mean combining a metaheuristic that applies
intensification and diversification incrementally at each step on partial solutions to create initial candidate
solutions which are then further explored by a metaheuristic that explores the space of complete candidate
solutions. The one-dimensional bin-packing problem was used as a case study to evaluate these ideas. The
hybridization of the developmental approach and genetic algorithm was found to perform better than each of
these approaches applied separately to solve the problem instances. This study was an initial attempt to test
the above hypothesis and has highlighted the potential of this hybridization. Given this future work will apply
this approach to other combinatorial optimization problems.
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