Genetic Algorithm with 3-parent Uniform Crossover

A new genetic algorithm which uses a 3-parent uniform crossover operator is developed and analyzed. Uniform crossover operators are shown to be based on the premise that all bit-level genetic information should be passed from parents to children. The 3-parent uniform crossover operator is shown to adhere to this premise. The 3-parent uniform crossover operator is shown to be better than the 2-parent uniform crossover operator on the De Jong test functions. Two new genetic algorithms which use 3-parent traditional crossover operators are developed and analyzed. The first uses a strategy of randomly selecting 3 of the 6 children resulting from 3-parent reproduction. The second uses a strategy of selecting the best 3 of the 6 children resulting from 3-parent reproduction. Each of the 3-parent traditional crossover operators is shown to be superior to the 2-parent traditional crossover operator on the De Jong test functions. The strategy of selecting the best 3 out of 6 children is shown to be superior to the strategy of randomly selecting 3 out of 6 children. In addition to these 3-parent genetic algorithms, a relationship between the Metropolis algorithm from simulated annealing and the two-membered evolution strategy is developed. The Metropolis algorithm is shown to be a special case of the two- membered evolution strategy

Explaining Adaptation in Genetic Algorithms With Uniform Crossover: The Hyperclimbing Hypothesis

The hyperclimbing hypothesis is a hypothetical explanation for adaptation in genetic algorithms with uniform crossover (UGAs). Hyperclimbing is an intuitive, general-purpose, non-local search heuristic applicable to discrete product spaces with rugged or stochastic cost functions. The strength of this heuristic lie in its insusceptibility to local optima when the cost function is deterministic, and its tolerance for noise when the cost function is stochastic. Hyperclimbing works by decimating a search space, i.e. by iteratively fixing the values of small numbers of variables. The hyperclimbing hypothesis holds that UGAs work by implementing efficient hyperclimbing. Proof of concept for this hypothesis comes from the use of a novel analytic technique involving the exploitation of algorithmic symmetry. We have also obtained experimental results that show that a simple tweak inspired by the hyperclimbing hypothesis dramatically improves the performance of a UGA on large, random instances of MAX-3SAT and the Sherrington Kirkpatrick Spin Glasses problem.Comment: 22 pages, 5 figure

A comparison of crossover operators in neural network feature selection with multiobjective evolutionary algorithms

Genetic algorithms are often employed for neural network feature selection. The efficiency of the search for a good subset of features, depends on the capability of the recombination operator to construct building blocks which perform well, based on existing genetic material. In this paper, a commonality-based crossover operator is employed, in a multiobjective evolutionary setting. The operator has two main characteristics: first, it exploits the concept that common schemata are more likely to form useful building blocks; second, the offspring produced are similar to their parents in terms of the subset size they encode. The performance of the novel operator is compared against that of uniform, 1 and 2-point crossover, in feature selection with probabilistic neural networks

Exact computation of the expectation curves for uniform crossover

Chicano, F., Whitley D., & Alba E. (2012). Exact computation of the expectation curves for uniform crossover. (Soule, T., & Moore J. H., Ed.).Genetic and Evolutionary Computation Conference, GECCO'12, Philadelphia, PA, USA, July 7-11, 2012. 1301–1308.Uniform crossover is a popular operator used in genetic algorithms to combine two tentative solutions of a problem represented as binary strings. We use the Walsh decomposition of pseudo-Boolean functions and properties of Krawtchouk matrices to exactly compute the expected value for the fitness of a child generated by uniform crossover from two parent solutions. We prove that this expectation is a polynomial in , the probability of selecting the best-parent bit. We provide efficient algorithms to compute this polynomial for ONEMAX and MAX-kSAT problems, but the results also hold for domains such as NK-Landscapes.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Spanish Ministry of Science and Innovation and FEDER under contract TIN2011-28194 (the roadME project). Andalusian Government under contract P07-TIC-03044 (DIRICOM project)

Влияние вида оператора скрещивания на эффективность поиска решения генетическим алгоритмом синтеза топологии телекоммуникационной сети

В статті наведено порівняльний аналіз впливу однорідного оператора схрещення та оператора схрещення з однією точкою розриву на ефективність пошуку рішення генетичним алгоритмом синтезу топології телекомунікаційної мережі. Отримані аналітичні залежності імовірності виникнення нової комбінації каналів зв'язку для заданого вузла телекомунікаційної системи в результаті застосування однорідного оператора схрещення та оператора схрещення з однією точкою розриву. In this paper we present the comparative analysis for one-point crossover and uniform crossover in genetic algorithms to identify the best topology of network. Presented Analytical dependences of probability of origin of a new combination of links for the set knot of network as a result of application of one-point crossover and uniform crossover

Investigation into the applications of genetic algorithms to control engineering

Bibliography: pages 117-120.This thesis report presents the results of a study carried out to determine possible uses of genetic algorithms to problems in control engineering. This thesis reviewed the literature on the subject of genetics and genetic algorithms and applied the algorithms to the problems of systems parameter identification and Pl/D controller tuning. More specifically, the study had the following objectives: To investigate possible uses of genetic algorithms to the task of system identification and Pl/D controller tuning. To do an in depth comparison of the proposed uses with orthodox traditional engineering thinking which is based on mathematical optimisation and empirical studies. To draw conclusions and present the findings in the form of a thesis. Genetic algorithms are a class of artificial intelligence methods inspired by the Darwinian principles of natural selection and survival of the fittest. The algorithm encodes potential solutions into chromosome-like data structures that. are evolved using genetic ·operators to determine the optimal solution of the problem. Fundamentally, the evolutionary nature of the algorithm is introduced through the operators called crossover and mutation. Crossover fundamentally takes two strings, selects a crossing point randomly and swaps segments of the strings on either side of the crossover point to create two new individuals. There are three variations of crossover which were considered in this thesis: single point crossover, two point crossover and uniform crossover. It was important that these be given careful consideration since much of the outcome of the algorithm is influenced by both the choice and the amount with which they are applied

How Crossover Speeds Up Building-Block Assembly in Genetic Algorithms

We re-investigate a fundamental question: how effective is crossover in Genetic Algorithms in combining building blocks of good solutions? Although this has been discussed controversially for decades, we are still lacking a rigorous and intuitive answer. We provide such answers for royal road functions and OneMax, where every bit is a building block. For the latter we show that using crossover makes every (\mu+\lambda) Genetic Algorithm at least twice as fast as the fastest evolutionary algorithm using only standard bit mutation, up to small-order terms and for moderate \mu and \lambda. Crossover is beneficial because it effectively turns fitness-neutral mutations into improvements by combining the right building blocks at a later stage. Compared to mutation-based evolutionary algorithms, this makes multi-bit mutations more useful. Introducing crossover changes the optimal mutation rate on OneMax from 1/n to (1+\sqrt{5})/2 \cdot 1/n \approx 1.618/n. This holds both for uniform crossover and k-point crossover. Experiments and statistical tests confirm that our findings apply to a broad class of building-block functions

Modified uniform crossover and desegregation in genetic algorithms

Describes a new crossover operator called modified uniform crossover, which in some circumstances works better than a uniform crossover. A new mutation operator called desegregation is empirically shown to improve the search process

Exact computation of the expectation surfaces for uniform crossover along with bit-flip mutation

Theoretical Computer Science 545, 2014, pp.76-93,Uniform crossover and bit-flip mutation are two popular operators used in genetic algorithms to generate new solutions in an iteration of the algorithm when the solutions are represented by binary strings. We use the Walsh decomposition of pseudo-Boolean functions and properties of Krawtchouk matrices to exactly compute the expected value for the fitness of a child generated by uniform crossover followed by bit-flip mutation from two parent solutions. We prove that this expectation is a polynomial in ρ, the probability of selecting the best-parent bit in the crossover, and μ, the probability of flipping a bit in the mutation. We provide efficient algorithms to compute this polynomial for Onemax and MAX-SAT problems, but the results also hold for other problems such as NK-Landscapes. We also analyze the features of the expectation surfaces.Spanish Ministry of Science and Innovation and FEDER under contract TIN2011-28194 (the roadME project). Air Force Office of Scientific Research, Air Force Materiel Command, USAF, under grant number FA9550-11-1-0088