In multimodal multiobjective optimization problems, multiple Pareto optimal sets, even some good local Pareto optimal sets, should be reserved, which can provide more choices for decision-makers. To solve MMOPs, this paper proposes an evolutionary algorithm with clustering-based assisted selection strategy for multimodal multiobjective optimization, in which the addition operator and deletion operator are proposed to comprehensively consider the diversity in both decision and objective spaces. Specifically, in decision space, the union population is partitioned into multiple clusters by using a (...) density-based clustering method, aiming to assist the addition operator to strengthen the population diversity. Then, a number of weight vectors are adopted to divide population into N subregions in objective space. Moreover, in the deletion operator, the solutions in the most crowded subregion are first collected into previous clusters, and then the worst solution in the most crowded cluster is deleted until there are N solutions left. Our algorithm is compared with other multimodal multiobjective evolutionary algorithms on the well-known benchmark MMOPs. Numerical experiments report the effectiveness and advantages of our proposed algorithm. (shrink)

Recently, decomposition-based multiobjective evolutionary algorithms have good performances in the field of multiobjective optimization problems and have been paid attention by many scholars. Generally, a MOP is decomposed into a number of subproblems through a set of weight vectors with good uniformly and aggregate functions. The main role of weight vectors is to ensure the diversity and convergence of obtained solutions. However, these algorithms with uniformity of weight vectors cannot obtain a set of solutions with good diversity (...) on some MOPs with complex Pareto optimal fronts. To deal with this problem, an improved decomposition-based multiobjective evolutionary algorithm with adaptive weight adjustment is proposed. Firstly, a new method based on uniform design and crowding distance is used to generate a set of weight vectors with good uniformly. Secondly, according to the distances of obtained nondominated solutions, an adaptive weight vector adjustment strategy is proposed to redistribute the weight vectors of subobjective spaces. Thirdly, a selection strategy is used to help each subobjective space to obtain a nondominated solution. Comparing with six efficient state-of-the-art algorithms, for example, NSGAII, MOEA/D, MOEA/D-AWA, EMOSA, RVEA, and KnEA on some benchmark functions, the proposed algorithm is able to find a set of solutions with better diversity and convergence. (shrink)

The effectiveness of evolutionary algorithms is one of the issues discussed in The Compatibility of Evolution and Design, where it is argued that such algorithms are only effective when stringent preconditions are met. This article considers this issue from the perspective of computer science. It explores the properties of problems that can be effectively solved by evolutionary algorithms, and the extent to which such algorithms need to be carefully adjusted. Although there are important differences (...) between the study of evolutionary algorithms in computer science and the study of biological evolution, it is argued that the experience of computer science can help to strengthen the claim that evolutionary mechanisms generally require a considerable degree of fine-tuning. (shrink)

Bound-constrained optimization has wide applications in science and engineering. In the last two decades, various evolutionary algorithms were developed under the umbrella of evolutionary computation for solving various bound-constrained benchmark functions and various real-world problems. In general, the developed evolutionary algorithms belong to nature-inspired algorithms and swarm intelligence paradigms. Differential evolutionary algorithm is one of the most popular and well-known EAs and has secured top ranks in most of the EA competitions in the (...) special session of the IEEE Congress on Evolutionary Computation. In this paper, a customized differential evolutionary algorithm is suggested and applied on twenty-nine large-scale bound-constrained benchmark functions. The suggested C-DE algorithm has obtained promising numerical results in its 51 independent runs of simulations. Most of the 2013 IEEE-CEC benchmark functions are tackled efficiently in terms of proximity and diversity. (shrink)

In the first part of our article we will refer the penetration of scientific terms into colloquial language, focusing on the sense in which the concept of an algorithm currently functions outside its original scope. The given examples will refer mostly to disciplines not directly related to computer science and to the colloquial language. In the next part we will also discuss the modifications made to the meaning of the term algorithm and how this concept is now understood in computer (...) science. Finally, we will discuss the problem of algorithmicity of evolutionary algorithms, i.e. we will try to answer the question whether – and to what extent – they are still algorithms in the classical sense. (shrink)

In this work, we present a new approach to solve the location management problem by using the reporting cells strategy. Location management is a very important and complex problem in mobile computing which aims to minimize the costs involved. In the reporting cells location management scheme, some cells in the network are designated as reporting cells . The choice of these cells is not trivial because they affect directly to the cost of the mobile network. This article is focused on (...) the use of high performance computing to execute a parallel heuristic that places optimally the RCs in a mobile network, minimizing its total cost. The main goal of this work is to demonstrate that the collaborative work of different evolutionary algorithms can obtain very good results. For this reason, we have implemented a parallel heuristic and six evolutionary algorithms that works in a parallel way on a cluster to solve the RCs problem. (shrink)

Multiobjective evolutionary algorithms have witnessed prosperity in solving many-objective optimization problems over the past three decades. Unfortunately, no one single MOEA equipped with given parameter settings, mating-variation operator, and environmental selection mechanism is suitable for obtaining a set of solutions with excellent convergence and diversity for various types of MaOPs. The reality is that different MOEAs show great differences in handling certain types of MaOPs. Aiming at these characteristics, this paper proposes a flexible ensemble framework, namely, ASES, which (...) is highly scalable for embedding any number of MOEAs to promote their advantages. To alleviate the undesirable phenomenon that some promising solutions are discarded during the evolution process, a big archive that number of contained solutions be far larger than population size is integrated into this ensemble framework to record large-scale nondominated solutions, and also an efficient maintenance strategy is developed to update the archive. Furthermore, the knowledge coming from updating archive is exploited to guide the evolutionary process for different MOEAs, allocating limited computational resources for efficient algorithms. A large number of numerical experimental studies demonstrated superior performance of the proposed ASES. Among 52 test instances, the ASES performs better than all the six baseline algorithms on at least half of the test instances with respect to both metrics hypervolume and inverted generational distance. (shrink)

The original design of a software system is rarely prepared for every new requirement. Software systems should be updated frequently, which is usually accompanied by the decline in software modularity and quality. Although many approaches have been proposed to improve the quality of software, a majority of them are guided by metrics defined on the local properties of software. In this article, we propose to use a global metric borrowed from the network science to detect the moving method refactoring. First, (...) our approach uses a bipartite network to represent classes, features, and their dependencies. Second, a new metric is introduced to quantify the modularity of a software system as a whole. Finally, a crossover-only evolutionary algorithm that uses the metric as its fitness function is introduced to optimize the class structure of a software system and detect the methods that should be moved. Empirical results on the benchmark Java projects show that our approach can find meaningful methods that should be moved with a high stability. The advantages of our approach are illustrated in comparison with some other approaches, specifically one refactoring approach, namely search-based refactoring approach, and two community detection algorithms, namely a graph theoretic clustering algorithm and a fast algorithm for community detection. Our approach provides a new way to do refactoring from the perspective of software structure. (shrink)

This paper proposes a constrained solution update strategy for multiobjective evolutionary algorithm based on decomposition, in which each agent aims to optimize one decomposed subproblem. Different from the existing approaches that assign one solution to each agent, our approach allocates the closest solutions to each agent and thus the number of solutions in an agent may be zero and no less than one. Regarding the agent with no solution, it will be assigned one solution in priority, once offspring are (...) generated closest to its subproblem. To keep the same population size, the agent with the largest number of solutions will remove one solution showing the worst convergence. This improves diversity for one agent, while the convergence of other agents is not lowered. On the agent with no less than one solution, offspring assigned to this agent are only allowed to update its original solutions. Thus, the convergence of this agent is enhanced, while the diversity of other agents will not be affected. After a period of evolution, our approach may gradually reach a stable status for solution assignment; i.e., each agent is only assigned with one solution. When compared to six competitive multiobjective evolutionary algorithms with different population selection or update strategies, the experiments validated the advantages of our approach on tackling two sets of test problems. (shrink)

In recent years, social networks have made significant progress and the number of people who use them to communicate is increasing day by day. The vast amount of information available on social networks has led to the importance of using friend recommender systems to discover knowledge about future communications. It is challenging to choose the best machine learning approach to address the recommender system issue since there are several strategies with various benefits and drawbacks. In light of this, a solution (...) based on the stacking approach was put out in this study to provide a buddy recommendation system in social networks. Additionally, a decrease in system performance was caused by the large amount of information that was accessible and the inefficiency of some functions. To solve this problem, a particle swarm optimization algorithm to select the most efficient features was used in our proposed method. To learn the model in the objective function of the particle swarm algorithm, a hybrid system based on stacking is proposed. In this method, two random forests and Extreme Gradient Boosting had been used as the base classifiers. The results obtained from these base classifiers were used in the logistic regression algorithm, which has been applied sequentially. The suggested approach was able to effectively address this issue by combining the advantages of the applied strategies. The results of implementation and evaluation of the proposed system show the appropriate efficiency of this method compared with other studied techniques. (shrink)

Neural algorithms are conserved during evolution. Neurons with different shapes and using different molecular mechanisms can perform the same computation. However, evolutionary conservation of neural algorithms is not sufficient for claiming the realization of an algorithm for a specific computational problem. A plausible scheme for ontogenetic emergence of the structure of the algorithm must also be provided.

In this paper, I propose a populational schema of modeling that consists of: a linear AFSV schema, and a higher-level schema employing the genetic algorithm. The basic ideas of the proposed solution are as follows: whole populations of models are considered at subsequent stages of the modeling process, successive populations are subjected to the activity of genetic operators and undergo selection procedures, the basis for selection is the evaluation function of the genetic algorithm. The schema can be applied to automate (...) the modeling of the mind/brain by means of artificial neural networks: the structure of each network is modified by genetic operators, modified networks undergo a learning cycle, and successive populations of networks are verified during the selection procedure. The whole process can be automated only partially, because it is the researcher who defines the evaluation function of the genetic algorithm. (shrink)

Estimation of Distribution Algorithms, which employ probabilistic models to generate the next population, are new promising methods in the field of genetic and evolutionary algorithms. In the case of conventional Genetic and Evolutionary Algorithms are applied to Constraint Satisfaction Problems, it is well-known that the incorporation of the domain knowledge in the Constraint Satisfaction Problems is quite effective. In this paper, we constitute a memetic algorithm as a combination of the Estimation of Distribution Algorithm and (...) a repair method. Experimental results on general Constraint Satisfaction Problems tell us the effectiveness of the proposed method. (shrink)

This paper considers two kinds of stochastic reentrant job shop scheduling problems, i.e., the SRJSSP with the maximum tardiness criterion and the SRJSSP with the makespan criterion. Owing to the NP-complete complexity of the considered RJSSPs, an effective differential evolutionary algorithm combined with two uncertainty handling techniques, namely, DEA_UHT, is proposed to address these problems. Firstly, to reasonably control the computation cost, the optimal computing budget allocation technique is applied for allocating limited computation budgets to assure reliable evaluation and (...) identification for excellent solutions or individuals, and the hypothesis test technique is added to execute a statistical comparison to reduce some unnecessary repeated evaluation. Secondly, a reentrant-largest-order-value rule is designed to convert the DEA’s individual to the SRJSSP’s solution. Thirdly, a conventional active decoding scheme for the job shop scheduling problem is extended to decode the solution for obtaining the criterion value. Fourthly, an Insert-based exploitation strategy and an Interchange-based exploration strategy are devised to enhance DEA’s exploitation ability and exploration ability, respectively. Finally, the test results and comparisons manifest the effectiveness and robustness of the proposed DEA_UHT. (shrink)

I examine the branch of evolutionary epistemology which tries to account for the character of cognitive mechanisms in animals and humans by extending the biological theory of evolution to the neurophysiological substrates of cognition. Like Plotkin, I construe this branch as a struggling science, and attempt to characterize the sort of theory one might expect to find this truly interdisciplinary endeavor, an endeavor which encompasses not only evolutionary biology, cognitive psychology, and developmental neuroscience, but also and especially, the (...) computational modeling of artificial life programming; I suggest that extending Schaffner''s notion of interlevel theories to include both horizontal and vertical levels of abstraction best fits the theories currently being developed in cognitive science. Finally, I support this claim with examples drawn from computational modeling data using the genetic algorithm. (shrink)