1,472 research outputs found
Fitness sharing and niching methods revisited
Interest in multimodal optimization function is expanding rapidly since real-world optimization problems often require the location of multiple optima in the search space. In this context, fitness sharing has been used widely to maintain population diversity and permit the investigation of many peaks in the feasible domain. This paper reviews various strategies of sharing and proposes new recombination schemes to improve its efficiency. Some empirical results are presented for high and a limited number of fitness function evaluations. Finally, the study
compares the sharing method with other niching techniques
Niching genetic algorithms for optimization in electromagnetics. I. Fundamentals
Niching methods extend genetic algorithms and permit the investigation of multiple optimal solutions in the search space. In this paper, we review and discuss various strategies of niching for optimization in electromagnetics. Traditional mathematical problems and an electromagnetic benchmark are solved using niching genetic algorithms to show their interest in real world optimization
Sub-structural Niching in Estimation of Distribution Algorithms
We propose a sub-structural niching method that fully exploits the problem
decomposition capability of linkage-learning methods such as the estimation of
distribution algorithms and concentrate on maintaining diversity at the
sub-structural level. The proposed method consists of three key components: (1)
Problem decomposition and sub-structure identification, (2) sub-structure
fitness estimation, and (3) sub-structural niche preservation. The
sub-structural niching method is compared to restricted tournament selection
(RTS)--a niching method used in hierarchical Bayesian optimization
algorithm--with special emphasis on sustained preservation of multiple global
solutions of a class of boundedly-difficult, additively-separable multimodal
problems. The results show that sub-structural niching successfully maintains
multiple global optima over large number of generations and does so with
significantly less population than RTS. Additionally, the market share of each
of the niche is much closer to the expected level in sub-structural niching
when compared to RTS
When Hillclimbers Beat Genetic Algorithms in Multimodal Optimization
It has been shown in the past that a multistart hillclimbing strategy
compares favourably to a standard genetic algorithm with respect to solving
instances of the multimodal problem generator. We extend that work and verify
if the utilization of diversity preservation techniques in the genetic
algorithm changes the outcome of the comparison. We do so under two scenarios:
(1) when the goal is to find the global optimum, (2) when the goal is to find
all optima.
A mathematical analysis is performed for the multistart hillclimbing
algorithm and a through empirical study is conducted for solving instances of
the multimodal problem generator with increasing number of optima, both with
the hillclimbing strategy as well as with genetic algorithms with niching.
Although niching improves the performance of the genetic algorithm, it is still
inferior to the multistart hillclimbing strategy on this class of problems.
An idealized niching strategy is also presented and it is argued that its
performance should be close to a lower bound of what any evolutionary algorithm
can do on this class of problems
Using Centroidal Voronoi Tessellations to Scale Up the Multi-dimensional Archive of Phenotypic Elites Algorithm
The recently introduced Multi-dimensional Archive of Phenotypic Elites
(MAP-Elites) is an evolutionary algorithm capable of producing a large archive
of diverse, high-performing solutions in a single run. It works by discretizing
a continuous feature space into unique regions according to the desired
discretization per dimension. While simple, this algorithm has a main drawback:
it cannot scale to high-dimensional feature spaces since the number of regions
increase exponentially with the number of dimensions. In this paper, we address
this limitation by introducing a simple extension of MAP-Elites that has a
constant, pre-defined number of regions irrespective of the dimensionality of
the feature space. Our main insight is that methods from computational geometry
could partition a high-dimensional space into well-spread geometric regions. In
particular, our algorithm uses a centroidal Voronoi tessellation (CVT) to
divide the feature space into a desired number of regions; it then places every
generated individual in its closest region, replacing a less fit one if the
region is already occupied. We demonstrate the effectiveness of the new
"CVT-MAP-Elites" algorithm in high-dimensional feature spaces through
comparisons against MAP-Elites in maze navigation and hexapod locomotion tasks
Clustering analysis of railway driving missions with niching
A wide number of applications requires classifying or grouping data into a set of categories or
clusters. Most popular clustering techniques to achieve this objective are K-means clustering and
hierarchical clustering. However, both of these methods necessitate the a priori setting of the cluster
number. In this paper, a clustering method based on the use of a niching genetic algorithm is presented,
with the aim of finding the best compromise between the inter-cluster distance maximization and the
intra-cluster distance minimization. This method is applied to three clustering benchmarks and to the
classification of driving missions for railway applications
Efficient genetic algorithms for solving hard constrained optimization problems
This paper studies many Genetic Algorithm strategies
to solve hard-constrained optimization problems. It investigates the role of various genetic operators to avoid premature convergence. In particular, an analysis of niching methods is carried out on a simple function to show advantages and drawbacks of each of them. Comparisons are also performed on an original benchmark based on an electrode shape optimization technique coupled with a charge simulation metho
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