683 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
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
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
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A niching memetic algorithm for simultaneous clustering and feature selection
Clustering is inherently a difficult task, and is made even more difficult when the selection of relevant features is also an issue. In this paper we propose an approach for simultaneous clustering and feature selection using a niching memetic algorithm. Our approach (which we call NMA_CFS) makes feature selection an integral part of the global clustering search procedure and attempts to overcome the problem of identifying less promising locally optimal solutions in both clustering and feature selection, without making any a priori assumption about the number of clusters. Within the NMA_CFS procedure, a variable composite representation is devised to encode both feature selection and cluster centers with different numbers of clusters. Further, local search operations are introduced to refine feature selection and cluster centers encoded in the chromosomes. Finally, a niching method is integrated to preserve the population diversity and prevent premature convergence. In an experimental evaluation we demonstrate the effectiveness of the proposed approach and compare it with other related approaches, using both synthetic and real data
Force-imitated particle swarm optimization using the near-neighbor effect for locating multiple optima
Copyright @ Elsevier Inc. All rights reserved.Multimodal optimization problems pose a great challenge of locating multiple optima simultaneously in the search space to the particle swarm optimization (PSO) community. In this paper, the motion principle of particles in PSO is extended by using the near-neighbor effect in mechanical theory, which is a universal phenomenon in nature and society. In the proposed near-neighbor effect based force-imitated PSO (NN-FPSO) algorithm, each particle explores the promising regions where it resides under the composite forces produced by the “near-neighbor attractor” and “near-neighbor repeller”, which are selected from the set of memorized personal best positions and the current swarm based on the principles of “superior-and-nearer” and “inferior-and-nearer”, respectively. These two forces pull and push a particle to search for the nearby optimum. Hence, particles can simultaneously locate multiple optima quickly and precisely. Experiments are carried out to investigate the performance of NN-FPSO in comparison with a number of state-of-the-art PSO algorithms for locating multiple optima over a series of multimodal benchmark test functions. The experimental results indicate that the proposed NN-FPSO algorithm can efficiently locate multiple optima in multimodal fitness landscapes.This work was supported in part by the Key Program of National Natural Science Foundation (NNSF) of China under Grant 70931001, Grant 70771021, and Grant 70721001, the National Natural Science Foundation (NNSF) of China for Youth under Grant 61004121, Grant 70771021, the Science Fund for Creative Research Group of NNSF of China under Grant 60821063, the PhD Programs Foundation of Ministry of Education of China under Grant 200801450008, and in part by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1 and Grant EP/E060722/2
A Genetic Programming Framework for Two Data Mining Tasks: Classification and Generalized Rule Induction
This paper proposes a genetic programming (GP) framework for two major data mining tasks, namely classification and generalized rule induction. The framework emphasizes the integration between a GP algorithm and relational database systems. In particular, the fitness of individuals is computed by submitting SQL queries to a (parallel) database server. Some advantages of this integration from a data mining viewpoint are scalability, data-privacy control and automatic parallelization
The True Destination of EGO is Multi-local Optimization
Efficient global optimization is a popular algorithm for the optimization of
expensive multimodal black-box functions. One important reason for its
popularity is its theoretical foundation of global convergence. However, as the
budgets in expensive optimization are very small, the asymptotic properties
only play a minor role and the algorithm sometimes comes off badly in
experimental comparisons. Many alternative variants have therefore been
proposed over the years. In this work, we show experimentally that the
algorithm instead has its strength in a setting where multiple optima are to be
identified
DISCOVERING INTERESTING PATTERNS FOR INVESTMENT DECISION MAKING WITH GLOWER C - A GENETIC LEARNER OVERLAID WITH ENTROPY REDUCTION
Prediction in financial domains is notoriously difficult for a number of reasons. First, theories tend to be
weak or non-existent, which makes problem formulation open-ended by forcing us to consider a large
number of independent variables and thereby increasing the dimensionality of the search space. Second, the
weak relationships among variables tend to be nonlinear, and may hold only in limited areas of the search
space. Third, in financial practice, where analysts conduct extensive manual analysis of historically well
performing indicators, a key is to find the hidden interactions among variables that perform well in
combination. Unfortunately, these are exactly the patterns that the greedy search biases incorporated by
many standard rule algorithms will miss. In this paper, we describe and evaluate several variations of a new
genetic learning algorithm (GLOWER) on a variety of data sets. The design of GLOWER has been motivated
by financial prediction problems, but incorporates successful ideas from tree induction and rule learning.
We examine the performance of several GLOWER variants on two UCI data sets as well as on a standard
financial prediction problem (S&P500 stock returns), using the results to identify and use one of the better
variants for further comparisons. We introduce a new (to KDD) financial prediction problem (predicting
positive and negative earnings surprises), and experiment withGLOWER, contrasting it with tree- and rule-induction
approaches. Our results are encouraging, showing that GLOWER has the ability to uncover
effective patterns for difficult problems that have weak structure and significant nonlinearities.Information Systems Working Papers Serie
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