111 research outputs found
Schemata as Building Blocks: Does Size Matter?
We analyze the schema theorem and the building block hypothesis using a
recently derived, exact schemata evolution equation. We derive a new schema
theorem based on the concept of effective fitness showing that schemata of
higher than average effective fitness receive an exponentially increasing
number of trials over time. The building block hypothesis is a natural
consequence in that the equation shows how fit schemata are constructed from
fit sub-schemata. However, we show that generically there is no preference for
short, low-order schemata. In the case where schema reconstruction is favoured
over schema destruction large schemata tend to be favoured. As a corollary of
the evolution equation we prove Geiringer's theorem. We give supporting
numerical evidence for our claims in both non-epsitatic and epistatic
landscapes.Comment: 17 pages, 10 postscript figure
Constructing dynamic test environments for genetic algorithms based on problem difficulty
This article is posted here with permission from IEEE - Copyright @ 2004 IEEEIn recent years the study of dynamic optimization problems has attracted an increasing interest from the community of genetic algorithms and researchers have developed a variety of approaches into genetic algorithms to solve these problems. In order to compare their performance, an important issue is the construction of standardized dynamic test environments. Based on the concept of problem difficulty, This work proposes a new dynamic environment generator using a decomposable trap function. With this generator, it is possible to systematically construct dynamic environments with changing and bounding difficulty and hence, we can test different genetic algorithms under dynamic environments with changing but controllable difficulty levels.This research was supported by UK EPSRC under Grant GR/S79718/01
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The use of some non-minimal representations to improve the effectiveness of genetic algorithms
In the unitation representation used in genetic algorithms, the number of genotypes that map onto each phenotype varies greatly. This leads to an attractor in phenotype space which impairs the performance of the genetic algorithm. The attractor is illustrated theoretically and empirically. A new representation, called the length varying representation (LVR), allows unitation chromosomes of varying length (and hence with a variety of attractors) to coexist. Chromosomes whose lengths yield attractors close to optima come to dominate the population. The LVR is shown to be more effective than the unitation representation against a variety of fitness functions. However, the LVR preferentially converges towards the low end of phenotype space. The phenotype shift representation (PSR), which retains the ability of the LVR to select for attractors that are close to optima, whilst using a fixed length chromosome and thus avoiding the asymmetries inherent in the LVR, is defined. The PSR is more effective than the LVR and the results compare favourably with previously published results from eight other algorithms. The internal operation of the PSR is investigated. The PSR is extended to cover multi-dimensional problems.
The premise that improvements in performance may be attained by the insertion of introns, non-coding sequences affecting linkage, into traditional bit string chromosomes is investigated. In this investigation, using a population size of 50, there was no evidence of improvement in performance. However, the position of the optima relative to the hamming cliffs is shown to have a major effect on the performance of the genetic algorithm using the binary representation, and the inadequacy of the traditional crossover and mutation operators in this context is demonstrated. Also, the disallowance of duplicate population members was found to improve performance over the standard generational replacement strategy in all trials
On the Runtime Analysis of the Clearing Diversity-Preserving Mechanism
Clearing is a niching method inspired by the principle of assigning the available resources
among a niche to a single individual. The clearing procedure supplies these resources only to
the best individual of each niche: the winner. So far, its analysis has been focused on experimental
approaches that have shown that clearing is a powerful diversity-preserving mechanism.
Using rigorous runtime analysis to explain how and why it is a powerful method, we prove that
a mutation-based evolutionary algorithm with a large enough population size, and a phenotypic
distance function always succeeds in optimising all functions of unitation for small niches
in polynomial time, while a genotypic distance function requires exponential time. Finally, we
prove that with phenotypic and genotypic distances clearing is able to find both optima for
Twomax and several general classes of bimodal functions in polynomial expected time. We
use empirical analysis to highlight some of the characteristics that makes it a useful mechanism
and to support the theoretical results
A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems
Copyright @ Springer-Verlag 2008Dynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This paper investigates the application of memetic algorithms, a class of hybrid evolutionary algorithms, for dynamic optimization problems. An adaptive hill climbing method is proposed as the local search technique in the framework of memetic algorithms, which combines the features of greedy crossover-based hill climbing and steepest mutation-based hill climbing. In order to address the convergence problem, two diversity maintaining methods, called adaptive dual mapping and triggered random immigrants, respectively, are also introduced into the proposed memetic algorithm for dynamic optimization problems. Based on a series of dynamic problems generated from several stationary benchmark problems, experiments are carried out to investigate the performance of the proposed memetic algorithm in comparison with some peer evolutionary algorithms. The experimental results show the efficiency of the proposed memetic algorithm in dynamic environments.This work was supported by the National Nature Science Foundation of China (NSFC) under Grant Nos. 70431003 and 70671020, the National Innovation Research Community Science Foundation of China under Grant No. 60521003, and the National Support Plan of China under Grant No. 2006BAH02A09 and the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01
Genetic algorithms with self-organizing behaviour in dynamic environments
Copyright @ 2007 Springer-VerlagIn recent years, researchers from the genetic algorithm (GA) community have developed several approaches to enhance the performance of traditional GAs for dynamic optimization problems (DOPs). Among these approaches, one technique is to maintain the diversity of the population by inserting random immigrants into the population. This chapter investigates a self-organizing random immigrants scheme for GAs to address DOPs, where the worst individual and its next neighbours are replaced by random immigrants. In order to protect the newly introduced immigrants from being replaced by fitter individuals, they are placed in a subpopulation. In this way, individuals start to interact between themselves and, when the fitness of the individuals are close, one single replacement of an individual can affect a large number of individuals of the population in a chain reaction. The individuals in a subpopulation are not allowed to be replaced by individuals of the main population during the current chain reaction. The number of individuals in the subpopulation is given by the number of individuals created in the current chain reaction. It is important to observe that this simple approach can take the system to a self-organization behaviour, which can be useful for GAs in dynamic environments.Financial support was obtained from FAPESP (Proc. 04/04289-6)
Runtime Analysis of Quality Diversity Algorithms
Quality diversity~(QD) is a branch of evolutionary computation that gained
increasing interest in recent years. The Map-Elites QD approach defines a
feature space, i.e., a partition of the search space, and stores the best
solution for each cell of this space. We study a simple QD algorithm in the
context of pseudo-Boolean optimisation on the ``number of ones'' feature space,
where the th cell stores the best solution amongst those with a number of
ones in . Here is a granularity parameter . We give a tight bound on the expected time until all cells are covered
for arbitrary fitness functions and for all and analyse the expected
optimisation time of QD on \textsc{OneMax} and other problems whose structure
aligns favourably with the feature space. On combinatorial problems we show
that QD finds a -approximation when maximising any monotone
sub-modular function with a single uniform cardinality constraint efficiently.
Defining the feature space as the number of connected components of a connected
graph, we show that QD finds a minimum spanning tree in expected polynomial
time
Do not Choose Representation just Change: An Experimental Study in States based EA
Our aim in this paper is to analyse the phenotypic effects (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
Analysis of the Clearing Diversity-Preserving Mechanism
Clearing is a niching method inspired by the principle of assigning the available resources among a subpopulation to a single individual. The clearing procedure supplies these resources only to the best individual of each subpopulation: the winner. So far, its analysis has been focused on experimental approaches that have shown that clearing is a powerful diversity mechanism. We use empirical analysis to highlight some of the characteristics that makes it a useful mechanism and runtime analysis to explain how and why it is a powerful method. We prove that a (mu+1) EA with large enough population size and a phenotypic distance function always succeeds in optimising all functions of unitation for small niches in polynomial time, while a genotypic distance function requires exponential time. Finally, we prove that a (mu+1) EA with phenotypic and genotypic distances is able to find both optima in TWOMAX for large niches in polynomial expected time
Memory-enhanced univariate marginal distribution algorithms for dynamic optimization problems
Several approaches have been developed into evolutionary algorithms to deal with dynamic optimization problems, of which memory and random immigrants are two major schemes. This paper investigates the application of a direct memory scheme for univariate marginal distribution algorithms (UMDAs), a class of evolutionary algorithms, for dynamic optimization problems. The interaction between memory and random immigrants for UMDAs in dynamic environments is also investigated. Experimental study shows that the memory scheme is efficient for UMDAs in dynamic environments and that the interactive effect between memory and random immigrants for UMDAs in dynamic environments depends on the dynamic environments
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