A bit-masking oriented data structure for evolutionary operators implementation in genetic algorithms

Improved random selection strategy, self-adaptive crossover and mutation operators, population co-evolution algorithm are adopted in this paper to set up a new genetic algorithm(GA), guaranteeing the universality of population, the thoroughness of solution and the execution efficiency of algorithm in solving multi-process, process crossover, work-piece flow and other condition based scheduling problem of flexible manufacturing system(FMS). The problem of GA in solving FMS scheduling is summarized in this paper, reasons and countermeasures for GA's pre-maturing are studied, longitudinal and transverse comprehensive state-based correlative self-adaptive crossover and mutation operators are raised according to the analysis on GA to avoid local optimization and pre-maturity, the co-evolutionary GA is designed to solve the scheduling model for FMS, and the algorithm is validated for the scheduling of box-type parts for precise machine tools.

With the emergence of wireless networking paradigm, several optimization problems are appearing in such networks. Such problem are related to optimizing network connectivity, coverage and stability. The resolution of these problems turns out to be crucial for optimized network performance. In the case of Wireless Mesh Networks, such problems include computing placement of mesh router nodes so that network performance is optimized. However, as these optimization problems are known to be computationally hard to solve, Genetic Algorithms (GAs) have been recently investigated as effective resolution methods. Mutation operator is one of the GA ingredients. Unlike crossover operators, which achieve to transmit genetic information from parents to off springs, mutation operators usually make some small local perturbation of the individuals, having thus less impact on individuals. Moreover, crossover is "a must" operator in GA and is usually applied with high probability, while mutation operators when implemented, are applied with small probability. Due to this, mutation operator is usually considered as a secondary operator. However, many studies in the literature have shown that mutation when effectively combined with selection operators can improve the performance of GAs. In this work we present the results of an experimental study on the effect of mutation and selection operators in GA for mesh router nodes placement problem. The study aims to identify the mutation and selection types that work best for instances of different characteristics.

Development of a Mutation Operator in a Real-Coded Genetic Algorithm for Bridge Model Optimization

BackgroundMining epistatic loci which affects specific phenotypic traits is an important research issue in the field of biology. Bayesian network (BN) is a graphical model which can express the relationship between genetic loci and phenotype. Until now, it has been widely used into epistasis mining in many research work. However, this method has two disadvantages: low learning efficiency and easy to fall into local optimum. Genetic algorithm has the excellence of rapid global search and avoiding falling into local optimum. It is scalable and easy to integrate with other algorithms. This work proposes an epistasis mining approach based on genetic tabu algorithm and Bayesian network (Epi-GTBN). It uses genetic algorithm into the heuristic search strategy of Bayesian network. The individual structure can be evolved through the genetic operations of selection, crossover and mutation. It can help to find the optimal network structure, and then further to mine the epistasis loci effectively. In order to enhance the diversity of the population and obtain a more effective global optimal solution, we use the tabu search strategy into the operations of crossover and mutation in genetic algorithm. It can help to accelerate the convergence of the algorithm.ResultsWe compared Epi-GTBN with other recent algorithms using both simulated and real datasets. The experimental results demonstrate that our method has much better epistasis detection accuracy in the case of not affecting the efficiency for different datasets.ConclusionsThe presented methodology (Epi-GTBN) is an effective method for epistasis detection, and it can be seen as an interesting addition to the arsenal used in complex traits analyses.

University examination timetabling problem is a complicated, multi-constraint combinatorial optimization problem. The current grouping genetic algorithm could get a better optimistic solution. Although the block encoding technique was applied in, the probabilities of crossover and mutation operation were still based on simple genetic algorithm. So the universality of this algorithm is not very well because the same probabilities of crossover and mutation operation could obtain different effects in different cases of examination arrangements. To find a more general algorithm, an Improved Adaptive Genetic Algorithm (IAGA) for practical applications was presented in this paper. IAGA can reconstruct the probabilities of crossover and mutation operation according to different cases of examination arrangements. To make further improvement of its convergence speed and overcome premature problem, IAGA appropriately adjusts mutation operation by Population's Maturity in addition. Moreover, the Memory Operator was added into the Algorithm to ensure getting the global optimization solution. Finally, IAGA was tested in function optimization and Examination Timetabling problems, and the experiments show that the results are promoted very well.

Proof searching and proof automation are the two most desired properties in interactive theorem provers (ITPs) as they generally require manual user guidance, which can be quite cumbersome. In this paper, we provide an evolutionary proof searching approach for the HOL4 proof assistant, where a genetic algorithm (GA) with different crossover and mutation operators is used to search and optimize the proofs in different HOL theories. Random proof sequences are first generated from a population of frequently occurring HOL4 proof steps that are discovered with sequential pattern mining. Generated proof sequences are then evolved with GA operators (three crossover and two mutation) till their fitness match the fitness of the target proof sequences. Various crossover and mutation operators are used to compare their effect on the performance of GAs in proof searching. Obtained results suggest that integrating GAs with HOL4 allows us to efficiently support proof finding and optimization.

Two new crossover operators in genetic algorithms for solving some combinatorial problems with ordering are presented. One is enhanced order crossover (EOX). The other, GREE, is a heuristic crossover for a class of combinatorial optimization problems, such as traveling salesman problems (TSPs). Genetic algorithms using GREE as unique crossover run very fast and get good solutions. Combining GREE with EOX, genetic algorithms can find optimal or very near optimal solutions in a rather short time. >

Bus scheduling problem is vital to ensure service quality and save operational cost. Electric bus scheduling problem is difficult to solve because of vehicles' limited driving range and charging requirements. This article investigates the multi-depot electric bus scheduling problem (MD-EBSP). A genetic algorithm with trip-adjustment strategy (GA-TAS) is proposed for the MD-EBSP. Firstly, a genetic algorithm (GA) with three crossover operators and seven mutation operators is devised to find a set of solutions. In each generation, a randomly selected crossover (mutation) operator is used to update the individuals. The solutions found by the GA are further improved by a trip-adjustment strategy (TAS) to obtain the final solution. The GA-TAS is compared with an adaptive large neighbourhood search method, a heuristic procedure, and experience-based scheduling schemes on seven problem instances. Experiments show that the GA-TAS outperforms comparative approaches on a number of vehicle and balancing vehicle driving tasks.

In order to improve the efficiency and reduce energy consumption of unmanned sailing boats for water quality inspection and sampling, particle swarm optimization was used for global path planning. The traditional particle swarm optimization algorithm has the problem of premature convergence because it is easy to fall into the local optimal solution. To solve this problem, an improved particle swarm optimization algorithm is proposed. Based on the traditional particle swarm optimization algorithm, the crossover and mutation operations in genetic algorithm are introduced. Firstly, the particle swarm was initialized and the fitness of the particle was calculated, and the individual optimal value and local optimal value were updated. Then crossover and mutation operations are introduced to update the particle velocity and position according to the results. Finally, the convergence of the algorithm is judged according to the decision criteria. The simulation results show that compared with the traditional particle swarm optimization algorithm, this algorithm can effectively avoid the algorithm falling into the local optimal solution, and greatly shorten the overall path length and reduce path planning time.

The assignment of fixed priorities to tasks and messages in distributed real-time systems is known to be an NP-hard problem, so there is no optimal method to accomplish it in polynomial time. Several generic search and optimization methods have been proposed in the literature for the priority-based scheduling of distributed real-time systems. The permutational genetic algorithm is one of those techniques that has shown a notable ability to solve this problem. The success of a genetic algorithm is greatly influenced by the used crossover and mutation operators, whose performance may vary between different problems. In this paper we make an introduction of some crossover and mutation operators for permutational genetic algorithms and make an experimental analysis of their performance in the assignment of fixed priorities to tasks and messages in distributed real-time systems.

We consider the generalized state space model (GSSM) which is an extension of the state space model to the non-Gaussian and non-linear model. There are serious problems in the GSSM approach because of the need for numerical integration over a state space. A Monte Carlo method for filtering and smoothing, called the Monte Carlo Filter (MCF), has been proposed to overcome this numerical problem. It has been pointed out that there exists a close relationship between the MCF and the genetic algorithm (GA) and that an essential structure involved in the MCF is quite similar to that in the GA. In this study, we try to replace the step of the prediction by the mutation and crossover operators in the GA, and demonstrate their performance as the system noise. We furthermore propose a smoothing algorithm in which a massively simple parallel procedure plays an important role. The proposed method is first applied to a simple problem and then to a seasonal adjustment for quarterly data sets in order to illustrate its broad applicability.

Algorithm composition is an automatic process which uses formalized strategies for music expression and creation. In this paper, we use Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) to simulate the process of Chinese music creation. A hybrid swarm intelligence composition model for Chinese music is proposed. In this model, the PSO is firstly used to generate the initial music material, and then melody is developed by using the evolutionary operators in GA. Finally, a piece of music work is formed. The experimental result shows that the algorithm is feasible, and has better performance than the genetic algorithm composition.

Floorplanning is a crucial process in the early stage of VLSI physical design. It determines the performance, reliability, and size of chips. B*-tree is a simple yet efficient representation that encodes the layout of modules in a compact and non-slicing structure. Several B*-tree variants and corresponding operators have been proposed to deal with non-slicing floorplanning. However, these operators are considered and applied individually. A collective manipulation of them remains missing. This study proposes a genetic algorithm (GA) that enables multiple crossover and mutation operators for solving the non-slicing floorplanning problem. In particular, the GA selects one crossover operator and one mutation operator from the pool of operators whenever reproducing an offspring. The probability for an operator to be selected is based on its empirical performance. This study conducts experiments on two well-known benchmarks to examine the effectiveness of the proposed method. The experimental results show that the GA can achieve superior solution quality and efficiency on the non-slicing VLSI floorplanning.

Developing Crew Allocation System for the Precast Industry Using Genetic Algorithms

Genetic Algorithm(GA) is a guided random search and the guiding direction always aims at the family whose ancestors have schemata with high fitness.Based on the results,the crossover operation in GA was redesigned by using the principle of random uniform design sampling and combining the locale search strategy.Then a new GA called Genetic Algorithm based on Uniform Design Sampling(UDS) was presented.The new GA was applied to solve the 2-way graph partitioning problem.Compared to simple GA and good point GA for solving this problem,the simulation results show that the new GA has superiority in terms of speed and accuracy and overcomes premature convergence.