13,444 research outputs found
Genetic Drift in Genetic Algorithm Selection Schemes
A method for calculating genetic drift in terms of changing population fitness variance is presented. The method allows for an easy comparison of different selection schemes and exact analytical results are derived for traditional generational selection, steady-state selection with varying generation gap, a simple model of Eshelman's CHC algorithm, and evolution strategies. The effects of changing genetic drift on the convergence of a GA are demonstrated empirically
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
Fitness Uniform Optimization
In evolutionary algorithms, the fitness of a population increases with time
by mutating and recombining individuals and by a biased selection of more fit
individuals. The right selection pressure is critical in ensuring sufficient
optimization progress on the one hand and in preserving genetic diversity to be
able to escape from local optima on the other hand. Motivated by a universal
similarity relation on the individuals, we propose a new selection scheme,
which is uniform in the fitness values. It generates selection pressure toward
sparsely populated fitness regions, not necessarily toward higher fitness, as
is the case for all other selection schemes. We show analytically on a simple
example that the new selection scheme can be much more effective than standard
selection schemes. We also propose a new deletion scheme which achieves a
similar result via deletion and show how such a scheme preserves genetic
diversity more effectively than standard approaches. We compare the performance
of the new schemes to tournament selection and random deletion on an artificial
deceptive problem and a range of NP-hard problems: traveling salesman, set
covering and satisfiability.Comment: 25 double-column pages, 12 figure
Numerical Complete Solution for Random Genetic Drift by Energetic Variational Approach
In this paper, we focus on numerical solutions for random genetic drift
problem, which is governed by a degenerated convection-dominated parabolic
equation. Due to the fixation phenomenon of genes, Dirac delta singularities
will develop at boundary points as time evolves. Based on an energetic
variational approach (EnVarA), a balance between the maximal dissipation
principle (MDP) and least action principle (LAP), we obtain the trajectory
equation. In turn, a numerical scheme is proposed using a convex splitting
technique, with the unique solvability (on a convex set) and the energy decay
property (in time) justified at a theoretical level. Numerical examples are
presented for cases of pure drift and drift with semi-selection. The remarkable
advantage of this method is its ability to catch the Dirac delta singularity
close to machine precision over any equidistant grid.Comment: 22 pages, 11 figures, 2 table
Improved sampling of the pareto-front in multiobjective genetic optimizations by steady-state evolution: a Pareto converging genetic algorithm
Previous work on multiobjective genetic algorithms has been focused on preventing genetic drift and the issue of convergence has been given little attention. In this paper, we present a simple steady-state strategy, Pareto Converging Genetic Algorithm (PCGA), which naturally samples the solution space and ensures population advancement towards the Pareto-front. PCGA eliminates the need for sharing/niching and thus minimizes heuristically chosen parameters and procedures. A systematic approach based on histograms of rank is introduced for assessing convergence to the Pareto-front, which, by definition, is unknown in most real search problems.
We argue that there is always a certain inheritance of genetic material belonging to a population, and there is unlikely to be any significant gain beyond some point; a stopping criterion where terminating the computation is suggested. For further encouraging diversity and competition, a nonmigrating island model may optionally be used; this approach is particularly suited to many difficult (real-world) problems, which have a tendency to get stuck at (unknown) local minima. Results on three benchmark problems are presented and compared with those of earlier approaches. PCGA is found to produce diverse sampling of the Pareto-front without niching and with significantly less computational effort
The use of frozen semen to minimize inbreeding in small populations
In this study, we compared the average coancestry and inbreeding levels for two genetic conservation schemes in which frozen semen from a gene bank is used to reduce the inbreeding in a live population. For a simple scheme in which only semen of generation-0 (G0) sires is used, the level of inbreeding asymptotes to 1/(2N), where N is the number of newborn sires in the base generation and rate of inbreeding goes to zero. However, when only sires of G0 are selected, all genes will eventually descend from the founder sires and all genes from the founder dams are lost. We propose an alternative scheme in which N sires from generation 1 (G1), as well as the N sires from G0, have semen conserved, and the semen of G0 and G1 sires is used for dams of odd and even generation numbers, respectively. With this scheme, the level of inbreeding asymptotes to 1/(3N) and the genes of founder dams are also conserved, because 50% of the genes of sires of G1 are derived from the founder dams. A computer simulation study shows that this is the optimum design to minimize inbreeding, even if semen from later generations is available
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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