Showing papers on "Crossover published in 2008"
TL;DR: It is shown that the proposed new version of DE, with the adaptive LS, performs better, or at least comparably, to classic DE algorithm.
Abstract: We propose a crossover-based adaptive local search (LS) operation for enhancing the performance of standard differential evolution (DE) algorithm. Incorporating LS heuristics is often very useful in designing an effective evolutionary algorithm for global optimization. However, determining a single LS length that can serve for a wide range of problems is a critical issue. We present a LS technique to solve this problem by adaptively adjusting the length of the search, using a hill-climbing heuristic. The emphasis of this paper is to demonstrate how this LS scheme can improve the performance of DE. Experimenting with a wide range of benchmark functions, we show that the proposed new version of DE, with the adaptive LS, performs better, or at least comparably, to classic DE algorithm. Performance comparisons with other LS heuristics and with some other well-known evolutionary algorithms from literature are also presented.
597 citations
TL;DR: This paper developed a hybrid genetic algorithm (GA) that uses two vectors to represent solutions and developed an efficient method to find assignable time intervals for the deleted operations based on the concept of earliest and latest event time.
470 citations
TL;DR: The result is a hybrid metaheuristic algorithm called Archive-Based hYbrid Scatter Search (AbYSS), which follows the scatter search structure but uses mutation and crossover operators from evolutionary algorithms, which outperforms the other two algorithms as regards the diversity of the solutions.
Abstract: We propose the use of a new algorithm to solve multiobjective optimization problems. Our proposal adapts the well-known scatter search template for single-objective optimization to the multiobjective domain. The result is a hybrid metaheuristic algorithm called Archive-Based hYbrid Scatter Search (AbYSS), which follows the scatter search structure but uses mutation and crossover operators from evolutionary algorithms. AbYSS incorporates typical concepts from the multiobjective field, such as Pareto dominance, density estimation, and an external archive to store the nondominated solutions. We evaluate AbYSS with a standard benchmark including both unconstrained and constrained problems, and it is compared with two state-of-the-art multiobjective optimizers, NSGA-II and SPEA2. The results obtained indicate that, according to the benchmark and parameter settings used, AbYSS outperforms the other two algorithms as regards the diversity of the solutions, and it obtains very competitive results according to the convergence to the true Pareto fronts and the hypervolume metric.
280 citations
TL;DR: The election of the most adequate evolution model to take out profit from these parent selection mechanisms is tackled and it is confirmed that these three processes may enhance the operation of the parent-centric crossover operators.
245 citations
TL;DR: HGA introduces several changes in the GA paradigm: a crossover operator specific for the RCPSP; a local improvement operator that is applied to all generated schedules; a new way to select the parents to be combined; and a two-phase strategy by which the second phase re-starts the evolution from a neighbour’s population of the best schedule.
240 citations
TL;DR: In this article, a new optimization technique based on a multiple tabu search algorithm (MTS) was proposed to solve the dynamic economic dispatch (ED) problem with generator constraints.
217 citations
TL;DR: In this article, two new self-adaptive member grouping strategies and a new strategy to set the initial population are discussed, and the effect of the proposed strategies on the performance of the GA for capturing the global optimum is tested on the optimization of 2d and 3d truss structures.
211 citations
TL;DR: A theoretical approach based on the graph and matroid theories (graphic matroid in particular) is considered in order to propose new intelligent and effective GA operators for efficient mutation and crossover well dedicated to the DN reconfiguration problem.
Abstract: This paper deals with distribution network (DN) reconfiguration for loss minimization. To solve this combinatorial problem, a genetic algorithm (GA) is considered. In order to enhance its ability to explore the solution space, efficient genetic operators are developed. After a survey of the existing DN topology description methods, a theoretical approach based on the graph and matroid theories (graphic matroid in particular) is considered. These concepts are used in order to propose new intelligent and effective GA operators for efficient mutation and crossover well dedicated to the DN reconfiguration problem. All resulting individuals after GA operators are claimed to be feasible (radial) configurations. Moreover, the presented approach is valid for planar or nonplanar DN graph topologies and avoids tedious mesh checks for the topology constraint validation. The proposed method is finally compared to some previous topology coding techniques used by other authors. The results show smaller or at least equal power losses with considerably less computation effort.
200 citations
TL;DR: This work presents a microarray-based method to analyze multiple aspects of crossover control simultaneously and rapidly, at high resolution, genome-wide, and on a cell-by-cell basis, and provides evidence to suggest that repression of crossing over at telomeres and centromeres arises from different mechanisms.
197 citations
TL;DR: In this paper, an improved genetic algorithm is introduced to find the optimal placement of sensors, where the modal strain energy (MSE) and modal assurance criterion (MAC) were taken as the fitness function, respectively, so that three placement designs were produced.
161 citations
TL;DR: In this article, the authors proposed a hybrid GA-SQP-GA algorithm for solving the economic dispatch problem (EDP) with valve-point loadings effects, where GA is used as the main optimizer and SQP is used to fine tune the solution of the GA run.
TL;DR: A field of science is sketched that uses both knowledge from classic literature and the newest discoveries to manage the occurrence of crossovers for a variety of breeding purposes.
26 Mar 2008
TL;DR: A new mechanism for studying the impact of subtree crossover in terms of semantic building blocks is presented, which allows to completely and compactly describe the semantic action of crossover, and provides insight into what does (or doesn't) make crossover effective.
Abstract: We present a new mechanism for studying the impact of subtree crossover in terms of semantic building blocks. This approach allows us to completely and compactly describe the semantic action of crossover, and provide insight into what does (or doesn't) make crossover effective. Our results make it clear that a very high proportion of crossover events (typically over 75% in our experiments) are guaranteed to perform no immediately useful search in the semantic space. Our findings also indicate a strong correlation between lack of progress and high proportions of fixed contexts. These results then suggest several new, theoretically grounded, research areas.
01 Jun 2008
TL;DR: A novel technique is presented, based on semantic analysis of programs, which forces each crossover to make candidate programs take a new step in the behavioural search space, which results in better performance and smaller solutions in two separate genetic programming experiments.
Abstract: Crossover forms one of the core operations in genetic programming and has been the subject of many different investigations. We present a novel technique, based on semantic analysis of programs, which forces each crossover to make candidate programs take a new step in the behavioural search space. We demonstrate how this technique results in better performance and smaller solutions in two separate genetic programming experiments.
TL;DR: Compared with other state-of-the-art evolutionary algorithms (EAs), this approach performs better, or at least comparably, in terms of the quality and stability of the final solutions.
TL;DR: It is proposed that both processes utilize a related mechanism involving changes in higher-order chromosome structure to achieve chromosome-wide effects, and a new role for condensin components in regulating crossover number and distribution is established.
Abstract: Biological processes that function chromosome-wide are not well understood. Here, we show that the Caenorhabditis elegans protein DPY-28 controls two such processes, X-chromosome dosage compensation in somatic cells and meiotic crossover number and distribution in germ cells. DPY-28 resembles a subunit of condensin, a conserved complex required for chromosome compaction and segregation. In the soma, DPY-28 associates with the dosage compensation complex on hermaphrodite X chromosomes to repress transcript levels. In the germline, DPY-28 restricts crossovers. In many organisms, one crossover decreases the likelihood of another crossover nearby, an enigmatic process called crossover interference. In C. elegans, interference is complete: Only one crossover occurs per homolog pair. dpy-28 mutations increase crossovers, disrupt crossover interference, and alter crossover distribution. Early recombination intermediates (RAD-51 foci) increase concomitantly, suggesting that DPY-28 acts to limit double-strand breaks (DSBs). Reinforcing this view, dpy-28 mutations partially restore DSBs in mutants lacking HIM-17, a chromatin-associated protein required for DSB formation. Our work further links dosage compensation to condensin and establishes a new role for condensin components in regulating crossover number and distribution. We propose that both processes utilize a related mechanism involving changes in higher-order chromosome structure to achieve chromosome-wide effects.
TL;DR: The particle swarm optimization is redefined and modified by introducing genetic operators such as crossover and mutation operator to update the particles to solve the job shop scheduling problem with fuzzy processing time.
TL;DR: This paper attempts to design an efficient GA based on a binary representation of arriving queues, which uses the neighboring relationship between each pair of aircraft, and the resulted chromosome is a 0-1-valued matrix.
Abstract: Arrival sequencing and scheduling (ASS) at airports is an NP-hard problem. Much effort has been made to use permutation-representation-based genetic algorithms (GAs) to tackle this problem, whereas this paper attempts to design an efficient GA based on a binary representation of arriving queues. Rather than using the order and/or arriving time of each aircraft in the queue to construct chromosomes for GAs, this paper uses the neighboring relationship between each pair of aircraft, and the resulted chromosome is a 0-1-valued matrix. A big advantage of this binary representation is a highly efficient uniform crossover operator, which is normally not applicable to those permutation representations. The strategy of receding horizon control (RHC) is also integrated into the new GA to attack the dynamic ASS problem. An extensive comparative simulation study shows that the binary-representation-based GA outperforms the permutation-representation-based GA.
12 Jul 2008
TL;DR: This is the first theoretical analysis proving the usefulness of crossover for a non-artificial problem and it is shown that the natural evolutionary algorithm for the all-pairs shortest path problem is significantly faster with a crossover operator than without.
Abstract: We show that the natural evolutionary algorithm for the all-pairs shortest path problem is significantly faster with a crossover operator than without. This is the first theoretical analysis proving the usefulness of crossover for a non-artificial problem.
TL;DR: This work shows an intimate connection between particle swarm optimisation (PSO) and evolutionary algorithms and generalises PSO to virtually any solution representation in a natural and straightforward way and applies naturally to both continuous and combinatorial spaces.
Abstract: Using a geometric framework for the interpretation of crossover of recent introduction, we show an intimate connection between particle swarm optimisation (PSO) and evolutionary algorithms. This connection enables us to generalise PSO to virtually any solution representation in a natural and straightforward way. The new geometric PSO (GPSO) applies naturally to both continuous and combinatorial spaces. We demonstrate this for the cases of Euclidean, Manhattan, and Hamming spaces and report extensive experimental results. We also demonstrate the applicability of GPSO to more challenging combinatorial spaces. The Sudoku puzzle is a perfect candidate to test new algorithmic ideas because it is entertaining and instructive as well as being a nontrivial constrained combinatorial problem. We apply GPSO to solve the Sudoku puzzle.
TL;DR: This work proposes a novel hybrid approach specialized for the ATSP that incorporates an improved genetic algorithm (IGA) and some optimization strategies that contribute to its effectiveness.
TL;DR: An optimal reactive power flow incorporating static voltage stability based on a multi-objective adaptive immune algorithm (MOAIA) can achieve a dynamic balance between individual diversity and population convergence.
01 Jan 2008
TL;DR: An elitist multi-objective genetic algorithm (EMOGA) for mining classification rules from large databases with a hybrid crossover operator for optimizing these objectives simultaneously with a clear edge over simple genetic algorithm.
Abstract: We present an elitist multi-objective genetic algorithm (EMOGA) for mining classification rules from large databases. We emphasize on predictive accuracy, comprehensibility and interestingness of the rules. However, predictive accuracy, comprehensibility and interestingness of the rules often conflict with each other. This makes it a multi-objective optimization problem that is very difficult to solve efficiently. We have proposed a multi-objective genetic algorithm with a hybrid crossover operator for optimizing these objectives simultaneously. We have compared our rule discovery procedure with simple genetic algorithm with a weighted sum of all these objectives. The experimental result confirms that our rule discovery algorithm has a clear edge over simple genetic algorithm.
TL;DR: This work relates tau to fluctuations of hydrogen bond network and shows that the crossover found for tau for both scenarios is a consequence of the sharp change in the average number of hydrogen bonds at the temperature of the specific heat maximum.
Abstract: Using Monte Carlo simulations and mean field calculations for a cell model of water we find a dynamic crossover in the orientational correlation time tau from non-Arrhenius behavior at high temperatures to Arrhenius behavior at low temperatures. This dynamic crossover is independent of whether water at very low temperature is characterized by a "liquid-liquid critical point" or by the "singularity-free" scenario. We relate tau to fluctuations of hydrogen bond network and show that the crossover found for tau for both scenarios is a consequence of the sharp change in the average number of hydrogen bonds at the temperature of the specific heat maximum. We find that the effect of pressure on the dynamics is strikingly different in the two scenarios, offering means to distinguish between them.
20 Jul 2008
TL;DR: A theoretical approach based on the graph and matroid theories is considered in order to propose new intelligent and effective GA operators for efficient mutation and crossover well dedicated to the DN reconfiguration problem.
Abstract: This paper deals with distribution network (DN) reconfiguration for loss minimization. To solve this combinatorial problem, a genetic algorithm (GA) is considered. In order to enhance its ability to explore the solution space, efficient genetic operators are developed. After a survey of the existing DN topology description methods, a theoretical approach based on the graph and matroid theories (graphic matroid in particular) is considered. These concepts are used in order to propose new intelligent and effective GA operators for efficient mutation and crossover well dedicated to the DN reconfiguration problem.
TL;DR: An attempt is made to hybridize these four GAs by incorporating the quadratic approximation (QA) operator into them, and it is concluded that the HGA3 outperforms all rest 7 versions.
TL;DR: The new ARMAX model for short-term load forecasting takes advantage of evolutionary strategy to speed up the convergence of particle swarm optimization (PSO), and applies the crossover operation of genetic algorithm to enhance the global search ability.
TL;DR: This study compares performances of eleven genetic crossover operators which have been widely used to solve other types of hard scheduling problems and finds a sequence of these jobs minimizing the sum of the weighted tardiness.
TL;DR: In this article, a cell model of water has been used to investigate the relationship between dynamics and thermodynamics, and the authors show that the crossover occurs at the locus of maximum isobaric specific heat in the pressure-temperature (P-T ) plane.
Abstract: On decreasing the temperature T , the correlation time τ of supercooled water displays a dynamic crossover from non-Arrhenius dynamics (with T -dependent activation energy) at high T to Arrhenius dynamics (with constant activation energy) at low T . Simulations for water models show that this crossover occurs at the locus of maximum isobaric specific heat in the pressure–temperature (P–T ) plane. Results of simulations show also that at this locus there is a sharp change of local structure: more tetrahedral below the locus, and less tetrahedral above it. Furthermore, in water solutions with proteins or DNA, simulations show that in correspondence with this locus there is a crossover in the dynamics of the biomolecules, a phenomenon commonly known as the protein glass transition. To clarify the relation of the dynamic crossover with the thermodynamics of water, we study the dynamics of a cell model of water which can be tuned to exhibit: (1) a first-order phase transition line that separates the liquids of high and low densities at low temperatures; this phase transition line terminates at a liquid–liquid critical point (LLCP), from which departs the Widom line TW(P), i.e. the line of maximum isobaric specific heat in the P–T plane; (2) the singularity-free (SF) scenario, under which the system exhibits water-like anomalies but with no finite temperature liquid–liquid critical point. We find that the dynamic crossover is present in both the LLCP and the SF cases. Moreover, on the basis of the study of the probability pB of forming a bond, we propose and verify a relation between dynamics and thermodynamics that is able to show how the crossover is a consequence of a local relaxation process associated with breaking a bond and reorienting the molecule. We further find a distinct difference in pressure dependence of the dynamic crossover between the LLCP and SF scenarios, which may help in resolving which of the scenarios correctly explains the anomalous behavior of water. (Some figures in this article are in colour only in the electronic version)
TL;DR: It is shown that an increase of the population size by a constant factor decreases the expected runtime from exponential to polynomial, and the best gap known so far is improved from superpolynomial vs. polynomatic to exponential vs. exponential.