Showing papers on "Crossover published in 2016"

Genetic Learning Particle Swarm Optimization

Abstract: Social learning in particle swarm optimization (PSO) helps collective efficiency, whereas individual reproduction in genetic algorithm (GA) facilitates global effectiveness. This observation recently leads to hybridizing PSO with GA for performance enhancement. However, existing work uses a mechanistic parallel superposition and research has shown that construction of superior exemplars in PSO is more effective. Hence, this paper first develops a new framework so as to organically hybridize PSO with another optimization technique for “learning.” This leads to a generalized “learning PSO” paradigm, the *L-PSO. The paradigm is composed of two cascading layers, the first for exemplar generation and the second for particle updates as per a normal PSO algorithm. Using genetic evolution to breed promising exemplars for PSO, a specific novel *L-PSO algorithm is proposed in the paper, termed genetic learning PSO (GL-PSO). In particular, genetic operators are used to generate exemplars from which particles learn and, in turn, historical search information of particles provides guidance to the evolution of the exemplars. By performing crossover, mutation, and selection on the historical information of particles, the constructed exemplars are not only well diversified, but also high qualified. Under such guidance, the global search ability and search efficiency of PSO are both enhanced. The proposed GL-PSO is tested on 42 benchmark functions widely adopted in the literature. Experimental results verify the effectiveness, efficiency, robustness, and scalability of the GL-PSO.

Hybrid feature selection based on enhanced genetic algorithm for text categorization

Abstract: An enhanced genetic algorithm (EGA) is proposed to reduce text dimensionality.The proposed EGA outperformed the traditional genetic algorithm.The EGA is incorporated with six filter feature selection methods to create hybrid feature selection approaches.The proposed hybrid approaches outperformed the single filtering methods. This paper proposes hybrid feature selection approaches based on the Genetic Algorithm (GA). This approach uses a hybrid search technique that combines the advantages of filter feature selection methods with an enhanced GA (EGA) in a wrapper approach to handle the high dimensionality of the feature space and improve categorization performance simultaneously. First, we propose EGA by improving the crossover and mutation operators. The crossover operation is performed based on chromosome (feature subset) partitioning with term and document frequencies of chromosome entries (features), while the mutation is performed based on the classifier performance of the original parents and feature importance. Thus, the crossover and mutation operations are performed based on useful information instead of using probability and random selection. Second, we incorporate six well-known filter feature selection methods with the EGA to create hybrid feature selection approaches. In the hybrid approach, the EGA is applied to several feature subsets of different sizes, which are ranked in decreasing order based on their importance, and dimension reduction is carried out. The EGA operations are applied to the most important features that had the higher ranks. The effectiveness of the proposed approach is evaluated by using naive Bayes and associative classification on three different collections of Arabic text datasets. The experimental results show the superiority of EGA over GA, comparisons of GA with EGA showed that the latter achieved better results in terms of dimensionality reduction, time and categorization performance. Furthermore, six proposed hybrid FS approaches consisting of a filter method and the EGA are applied to various feature subsets. The results showed that these hybrid approaches are more effective than single filter methods for dimensionality reduction because they were able to produce a higher reduction rate without loss of categorization precision in most situations.

Disassembly Sequence Optimization for Large-Scale Products With Multiresource Constraints Using Scatter Search and Petri Nets

Abstract: Disassembly modeling and planning are meaningful and important to the reuse, recovery, and recycling of obsolete and discarded products. However, the existing methods pay little or no attention to resources constraints, e.g., disassembly operators and tools. Thus a resulting plan when being executed may be ineffective in actual product disassembly. This paper proposes to model and optimize selective disassembly sequences subject to multiresource constraints to maximize disassembly profit. Moreover, two scatter search algorithms with different combination operators, namely one with precedence preserved crossover combination operator and another with path-relink combination operator, are designed to solve the proposed model. Their validity is shown by comparing them with the optimization results from well-known optimization software CPLEX for different cases. The experimental results illustrate the effectiveness of the proposed method.

An improved teaching-learning-based optimization algorithm for numerical and engineering optimization problems

Abstract: The teaching-learning-based optimization (TLBO) algorithm, one of the recently proposed population-based algorithms, simulates the teaching-learning process in the classroom. This study proposes an improved TLBO (ITLBO), in which a feedback phase, mutation crossover operation of differential evolution (DE) algorithms, and chaotic perturbation mechanism are incorporated to significantly improve the performance of the algorithm. The feedback phase is used to enhance the learning style of the students and to promote the exploration capacity of the TLBO. The mutation crossover operation of DE is introduced to increase population diversity and to prevent premature convergence. The chaotic perturbation mechanism is used to ensure that the algorithm can escape the local optimal. Simulation results based on ten unconstrained benchmark problems and five constrained engineering design problems show that the ITLBO algorithm is better than, or at least comparable to, other state-of-the-art algorithms.

Evolutionary multi-objective optimization algorithms for fuzzy portfolio selection

Abstract: Graphical abstractDisplay Omitted HighlightsWe consider a constrained three-objective optimization portfolio selection problem.We solve the problem by means of evolutionary multi-objective optimization.New mutation, crossover and reparation operators are designed for this problem.They are tested in several algorithms for a data set from the Spanish stock market.Results for two performance metrics reveal the effectiveness of the new operators. In this paper, we consider a recently proposed model for portfolio selection, called Mean-Downside Risk-Skewness (MDRS) model. This modelling approach takes into account both the multidimensional nature of the portfolio selection problem and the requirements imposed by the investor. Concretely, it optimizes the expected return, the downside-risk and the skewness of a given portfolio, taking into account budget, bound and cardinality constraints. The quantification of the uncertain future return on a given portfolio is approximated by means of LR-fuzzy numbers, while the moments of its return are evaluated using possibility theory. The main purpose of this paper is to solve the MDRS portfolio selection model as a whole constrained three-objective optimization problem, what has not been done before, in order to analyse the efficient portfolios which optimize the three criteria simultaneously. For this aim, we propose new mutation, crossover and reparation operators for evolutionary multi-objective optimization, which have been specially designed for generating feasible solutions of the cardinality constrained MDRS problem. We incorporate the operators suggested into the evolutionary algorithms NSGAII, MOEA/D and GWASF-GA and we analyse their performances for a data set from the Spanish stock market. The potential of our operators is shown in comparison to other commonly used genetic operators and some conclusions are highlighted from the analysis of the trade-offs among the three criteria.

Multi-objective backtracking search algorithm for economic emission dispatch problem

Abstract: Economic/emission dispatch problem (EED) is solved.Transmission network loss and valve-point effects are considered.Multi-objective backtracking search algorithm (MOBSA) is developed.Weighted sum method (WSM) and non-dominated approach (NDA) are employed.The proposed method is able to solve EED problems efficiently. This paper presents the application of backtracking search algorithm (BSA) for solving an economic/emission dispatch (EED) problem as a multi-objective optimization problem. BSA is a newly developed evolutionary algorithm with one control parameter to solve numerical optimization problems. It utilizes crossover and mutation operators to advance optimization toward the optimal. The multi-objective BSA developed and presented in this paper uses an elitist external archive to store non-dominated solutions known as pareto front. The problem of EED is also solved by weighted sum method, which combines both objectives of the problem into a single objective. Three test systems are the case studies verifying the effectiveness of BSA. The results are compared with those of other methods in literatures and confirm the high performance of BSA.

A Hybrid Evolutionary Immune Algorithm for Multiobjective Optimization Problems

Abstract: In recent years, multiobjective immune algorithms (MOIAs) have shown promising performance in solving multiobjective optimization problems (MOPs). However, basic MOIAs only use a single hypermutation operation to evolve individuals, which may induce some difficulties in tackling complicated MOPs. In this paper, we propose a novel hybrid evolutionary framework for MOIAs, in which the cloned individuals are divided into several subpopulations and then evolved using different evolutionary strategies. An example of this hybrid framework is implemented, in which simulated binary crossover and differential evolution with polynomial mutation are adopted. A fine-grained selection mechanism and a novel elitism sharing strategy are also adopted for performance enhancement. Various comparative experiments are conducted on 28 test MOPs and our empirical results validate the effectiveness and competitiveness of our proposed algorithm in solving MOPs of different types.

Algebraic Differential Evolution Algorithm for the Permutation Flowshop Scheduling Problem With Total Flowtime Criterion

Abstract: This paper introduces an original algebraic approach to differential evolution (DE) algorithms for combinatorial search spaces. An abstract algebraic differential mutation for generic combinatorial spaces is defined by exploiting the concept of a finitely generated group. This operator is specialized for the permutations space by means of an original randomized bubble sort algorithm. Then, a discrete DE algorithm is derived for permutation problems and it is applied to the permutation flowshop scheduling problem with the total flowtime criterion. Other relevant components of the proposed algorithm are: a crossover operator for permutations, a novel biased selection strategy, a heuristic-based initialization, and a memetic restart procedure. Extensive experimental tests have been performed on a widely accepted benchmark suite in order to analyze the dynamics of the proposed approach and to compare it with the state-of-the-art algorithms. The experimental results clearly show that the proposed algorithm reaches state-of-the-art performances and, most remarkably, it is able to find some new best known results. Furthermore, the experimental analysis on the impact of the algorithmic components shows that the two main contributions of this paper, i.e., the discrete differential mutation and the biased selection operator, greatly contribute to the overall performance of the algorithm.

The challenge of evolving stable polyploidy: could an increase in “crossover interference distance” play a central role?

Abstract: Whole genome duplication is a prominent feature of many highly evolved organisms, especially plants. When duplications occur within species, they yield genomes comprising multiple identical or very similar copies of each chromosome ("autopolyploids"). Such genomes face special challenges during meiosis, the specialized cellular program that underlies gamete formation for sexual reproduction. Comparisons between newly formed (neo)-autotetraploids and fully evolved autotetraploids suggest that these challenges are solved by specific restrictions on the positions of crossover recombination events and, thus, the positions of chiasmata, which govern the segregation of homologs at the first meiotic division. We propose that a critical feature in the evolution of these more effective chiasma patterns is an increase in the effective distance of meiotic crossover interference, which plays a central role in crossover positioning. We discuss the findings in several organisms, including the recent identification of relevant genes in Arabidopsis arenosa, that support this hypothesis.

A new genetic algorithm approach to smooth path planning for mobile robots

Abstract: Purpose The purpose of this paper is to consider the smooth path planning problem for a mobile robot based on the genetic algorithm (GA) and the Bezier curve. Design/methodology/approach The workspace of a mobile robot is described by a new grid-based representation that facilitates the operations of the adopted GA. The chromosome of the GA is composed of a sequence of binary numbered grids (i.e. control points of the Bezier curve). Ordinary genetic operators including crossover and mutation are used to search the optimum chromosome where the optimization criterion is the length of a piecewise collision-free Bezier curve path determined by the control points. Findings This paper has proposed a new smooth path planning for a mobile robot by resorting to the GA and the Bezier curve. A new grid-based representation of the workspace has been presented, which makes it convenient to perform operations in the GA. The GA has been used to search the optimum control points that determine the Bezier curve-based smooth path. The effectiveness of the proposed approach has been verified by a numerical experiment, and some performances of the obtained method have also been analyzed. Research limitations/implications There still remain many interesting topics, for example, how to solve the specific smooth path planning problem by using the GA and how to promote the computational efficiency in the more grids case. These issues deserve further research. Originality/value The purpose of this paper is to improve the existing results by making the following three distinctive contributions: a rigorous mathematical formulation of the path planning optimization problem is formulated; a general grid-based representation (2n × 2n) is proposed to describe the workspace of the mobile robots to facilitate the implementation of the GA where n is chosen according to the trade-off between the accuracy and the computational burden; and the control points of the Bezier curve are directly linked to the optimization criteria so that the generated paths are guaranteed to be optimal without any need for smoothing afterwards.

Crossover Designs in Software Engineering Experiments: Benefits and Perils

Abstract: In experiments with crossover design subjects apply more than one treatment. Crossover designs are widespread in software engineering experimentation: they require fewer subjects and control the variability among subjects. However, some researchers disapprove of crossover designs. The main criticisms are: the carryover threat and its troublesome analysis. Carryover is the persistence of the effect of one treatment when another treatment is applied later. It may invalidate the results of an experiment. Additionally, crossover designs are often not properly designed and/or analysed, limiting the validity of the results. In this paper, we aim to make SE researchers aware of the perils of crossover experiments and provide risk avoidance good practices. We study how another discipline (medicine) runs crossover experiments. We review the SE literature and discuss which good practices tend not to be adhered to, giving advice on how they should be applied in SE experiments. We illustrate the concepts discussed analysing a crossover experiment that we have run. We conclude that crossover experiments can yield valid results, provided they are properly designed and analysed, and that, if correctly addressed, carryover is no worse than other validity threats.

Escaping Local Optima with Diversity Mechanisms and Crossover

Abstract: Population diversity is essential for the effective use of any crossover operator. We compare seven commonly used diversity mechanisms and prove rigorous run time bounds for the (μ+1) GA using uniform crossover on the fitness function Jumpk. All previous results in this context only hold for unrealistically low crossover probability pc=O(k/n), while we give analyses for the setting of constant pc k, the population size μ, and the crossover probability pc. For the typical case of constant k > 2 and constant pc, we can compare the resulting expected optimisation times for different diversity mechanisms assuming an optimal choice of μ: O}(nk-1) for duplicate elimination/minimisation, O}(n2 log n) for maximising the convex hull, O(n log n) for det. crowding (assuming pc = k/n), O(n log n) for maximising the Hamming distance, O(n log n) for fitness sharing, O(n log n) for the single-receiver island model. This proves a sizeable advantage of all variants of the (μ+1) GA compared to the (1+1) EA, which requires θ(nk). In a short empirical study we confirm that the asymptotic differences can also be observed experimentally.

High-Resolution Mapping of Crossover and Non-crossover Recombination Events by Whole-Genome Re-sequencing of an Avian Pedigree.

Abstract: Recombination is an engine of genetic diversity and therefore constitutes a key process in evolutionary biology and genetics. While the outcome of crossover recombination can readily be detected as shuffled alleles by following the inheritance of markers in pedigreed families, the more precise location of both crossover and non-crossover recombination events has been difficult to pinpoint. As a consequence, we lack a detailed portrait of the recombination landscape for most organisms and knowledge on how this landscape impacts on sequence evolution at a local scale. To localize recombination events with high resolution in an avian system, we performed whole-genome re-sequencing at high coverage of a complete three-generation collared flycatcher pedigree. We identified 325 crossovers at a median resolution of 1.4 kb, with 86% of the events localized to <10 kb intervals. Observed crossover rates were in excellent agreement with data from linkage mapping, were 52% higher in male (3.56 cM/Mb) than in female meiosis (2.28 cM/Mb), and increased towards chromosome ends in male but not female meiosis. Crossover events were non-randomly distributed in the genome with several distinct hot-spots and a concentration to genic regions, with the highest density in promoters and CpG islands. We further identified 267 non-crossovers, whose location was significantly associated with crossover locations. We detected a significant transmission bias (0.18) in favour of 'strong' (G, C) over 'weak' (A, T) alleles at non-crossover events, providing direct evidence for the process of GC-biased gene conversion in an avian system. The approach taken in this study should be applicable to any species and would thereby help to provide a more comprehensive portray of the recombination landscape across organism groups.

Utilizing cumulative population distribution information in differential evolution

Abstract: Display OmittedThe mutation, crossover, and selection of CPI-DE. Due to the fact that single population fails to contain enough information to estimate the covariance matrix reliably, this paper updates the covariance matrix in DE by an adaptation procedure, which makes use of the cumulative distribution information of the population.CPI-DE provides a simple yet efficient synergy of two kinds of crossover: the crossover in the Eigen coordinate system and the crossover in the original coordinate system. The former aims at identifying the properties of the fitness landscape and improving the efficiency and effectiveness of DE by producing the offspring toward promising directions. In addition, the purpose of the latter is to maintain the superiority of the original DE. Moreover, no extra parameters are required in CPI-DE.Our experimental studies have shown that CPI-DE is capable of enhancing the performance of several classic DE versions and advanced DE variants. Differential evolution (DE) is one of the most popular paradigms of evolutionary algorithms. In general, DE does not exploit distribution information provided by the population and, as a result, its search performance is limited. In this paper, cumulative population distribution information of DE has been utilized to establish an Eigen coordinate system by making use of covariance matrix adaptation. The crossover operator of DE implemented in the Eigen coordinate system has the capability to identify the features of the fitness landscape. Furthermore, we propose a cumulative population distribution information based DE framework called CPI-DE. In CPI-DE, for each target vector, two trial vectors are generated based on both the original coordinate system and the Eigen coordinate system. Then, the target vector is compared with these two trial vectors and the best one will survive into the next generation. CPI-DE has been applied to two classic versions of DE and three state-of-the-art variants of DE for solving two sets of benchmark test functions, namely, 28 test functions with 30 and 50 dimensions at the 2013 IEEE Congress on Evolutionary Computation, and 30 test functions with 30 and 50 dimensions at the 2014 IEEE Congress on Evolutionary Computation. The experimental results suggest that CPI-DE is an effective framework to enhance the performance of DE.

A multi-agent genetic algorithm for community detection in complex networks

Abstract: Complex networks are popularly used to represent a lot of practical systems in the domains of biology and sociology, and the structure of community is one of the most important network attributes which has received an enormous amount of attention. Community detection is the process of discovering the community structure hidden in complex networks, and modularity Q is one of the best known quality functions measuring the quality of communities of networks. In this paper, a multi-agent genetic algorithm, named as MAGA-Net, is proposed to optimize modularity value for the community detection. An agent, coded by a division of a network, represents a candidate solution. All agents live in a lattice-like environment, with each agent fixed on a lattice point. A series of operators are designed, namely split and merging based neighborhood competition operator, hybrid neighborhood crossover, adaptive mutation and self-learning operator, to increase modularity value. In the experiments, the performance of MAGA-Net is validated on both well-known real-world benchmark networks and large-scale synthetic LFR networks with 5000 nodes. The systematic comparisons with GA-Net and Meme-Net show that MAGA-Net outperforms these two algorithms, and can detect communities with high speed, accuracy and stability.

A Survey on Crossover Operators

Abstract: Crossover is an important operation in the Genetic Algorithms (GA). Crossover operation is responsible for producing offspring for the next generation so as to explore a much wider area of the solution space. There are many crossover operators designed to cater to different needs of different optimization problems. Despite the many analyses, it is still difficult to decide which crossover to use when. In this article, we have considered the various existing crossover operators based on the application for which they were designed for and the purpose that they were designed for. We have classified the existing crossover operators into two broad categories, namely (1) Crossover operators for representation of applications -- where the crossover operators designed to suit the representation aspect of applications are discussed along with how the crossover operators work and (2) Crossover operators for improving GA performance of applications -- where crossover operators designed to influence the quality of the solution and speed of GA are discussed. We have also come up with some interesting future directions in the area of designing new crossover operators as a result of our survey.

Quantum Genetic Algorithms for Computer Scientists

Abstract: Genetic algorithms (GAs) are a class of evolutionary algorithms inspired by Darwinian natural selection. They are popular heuristic optimisation methods based on simulated genetic mechanisms, i.e., mutation, crossover, etc. and population dynamical processes such as reproduction, selection, etc. Over the last decade, the possibility to emulate a quantum computer (a computer using quantum-mechanical phenomena to perform operations on data) has led to a new class of GAs known as “Quantum Genetic Algorithms” (QGAs). In this review, we present a discussion, future potential, pros and cons of this new class of GAs. The review will be oriented towards computer scientists interested in QGAs “avoiding” the possible difficulties of quantum-mechanical phenomena.

Path planning for intelligent robot based on switching local evolutionary PSO algorithm

Abstract: Purpose This paper aims to present a novel particle swarm optimization (PSO) based on a non-homogeneous Markov chain and differential evolution (DE) for path planning of intelligent robot when having obstacles in the environment. Design/methodology/approach The three-dimensional path surface of the intelligent robot is decomposed into a two-dimensional plane and the height information in z axis. Then, the grid method is exploited for the environment modeling problem. After that, a recently proposed switching local evolutionary PSO (SLEPSO) based on non-homogeneous Markov chain and DE is analyzed for the path planning problem. The velocity updating equation of the presented SLEPSO algorithm jumps from one mode to another based on the non-homogeneous Markov chain, which can overcome the contradiction between local and global search. In addition, DE mutation and crossover operations can enhance the capability of finding a better global best particle in the PSO method. Findings Finally, the SLEPSO algorithm is successfully applied to the path planning in two different environments. Comparing with some well-known PSO algorithms, the experiment results show the feasibility and effectiveness of the presented method. Originality/value Therefore, this can provide a new method for the area of path planning of intelligent robot.

A High-Performance Complexity Reduced Behavioral Model and Digital Predistorter for MIMO Systems With Crosstalk

Abstract: In this paper, an augmented crossover memory polynomial model (A-COMPM) is proposed and developed which can be used for characterizing and linearizing multiple-input multiple-output (MIMO) transmitters in the presence of linear and nonlinear crosstalk. The proposed model significantly improves the performance of the crossover memory polynomial model (CO-MPM) by more accurately incorporating the effect of crosstalk. The proposed model performs comparably to the $2\times 2$ parallel Hammerstein ( $2\times 2$ PH) model, while requiring the same number of coefficients as CO-MPM and a lower number of coefficients than the $2\times 2$ PH. The model was tested for forward modeling and digital predistortion (DPD) applications in the presence of both linear and nonlinear crosstalk. Experimental results show that the model outperforms the CO-MPM and $2\times 2$ PH DPDs, at a lower number of coefficients compared to both models. Furthermore, the issue of numerical stability of the DPD extraction and implementation procedures is addressed in the model.