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4th International Conference on Artificial Immune Systems

作者： Cutello, V Narzisi, G Nicosia, G Pavone, M Univ Catania Dept Math & Comp Sci I-95125 Catania Italy

ISBN: (纸本)3540281754

This paper presents a comparative study of two important clonal selection algorithms (CSAs): clonalG and opt-IA. To deeply understand the performance of both algorithms, we deal with four different classes of problems: toy problems (one-counting and trap functions), pattern recognition, numerical optimization problems and NP-complete problem (the 2D HP model for protein structure prediction problem). Two possible versions of clonalG have been implemented and tested. The experimental results show a global better performance of opt-IA with respect to clonalG. Considering the results obtained, we can claim that CSAs represent a new class of Evolutionary algorithms for effectively performing searching, learning and optimization tasks.

关键词： clonal selection algorithms clonalG opt-IA onecounting trap functions pattern recognition numerical optimization NP-complete problems 2D HP protein structure prediction

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An Information-Theoretic Approach for clonal selection algorithms

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9th International Conference on Artificial Immune Systems (ICARIS 2010)

作者： Cutello, Vincenzo Nicosia, Giuseppe Pavone, Mario Stracquadanio, Giovanni Univ Catania Dept Math & Comp Sci I-95125 Catania Italy

ISBN: (纸本)9783642145469

In this research work a large set of the classical numerical functions were taken into account in order to understand both the search capability and the ability to escape from a local optimal of a clonal selection algorithm, called i-CSA. The algorithm was extensively compared against several variants of Differential Evolution (DE) algorithm, and with some typical swarm intelligence algorithms. The obtained results show as i-CSA is effective in terms of accuracy, and it is able to solve large-scale instances of well-known benchmarks. Experimental results also indicate that the algorithm is comparable, and often outperforms, the compared nature-inspired approaches. From the experimental results, it is possible to note that a longer maturation of a B cell, inside the population, assures the achievement of better solutions;the maturation period affects the diversity and the effectiveness of the immune search process on a specific problem instance. To assess the learning capability during the evolution of the algorithm three different relative entropies were used: Kullback-Leibler, Renyi generalized and Von Neumann divergences. The adopted entropic divergences show a strong correlation between optima discovering, and high relative entropy values.

关键词： clonal selection algorithms population-based algorithms information theory relative entropy global numerical optimization

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Recent Advances in clonal selection algorithms and Applications

Recent Advances in Clonal Selection Algorithms and Applicati...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Luo, Wenjian Lin, Xin Univ Sci & Technol China Sch Comp Sci & Technol Anhui Prov Key Lab Software Engn Comp & Commun Hefei 230027 Anhui Peoples R China

ISBN: (纸本)9781538627266

clonal selection algorithms (CSAs) are a kind of Artificial Immune algorithms (AIAs). In this paper, recent advances in clonal selection algorithms are summarized and reviewed. First, the basic framework of clonal selection algorithms is given. Second, various types of applications using clonal selection algorithms are summarized, including global optimization, constrained optimization, combinatorial optimization, multiobjective optimization, dynamic optimization and other applications. Last, a brief conclusion and some remarks about clonal selection algorithms are given.

关键词： Immune Computation clonal selection algorithms Optimization

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Modifying Regeneration Mutation and Hybridising clonal selection for Evolutionary algorithms Based Timetabling Tool

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MATHEMATICAL PROBLEMS IN ENGINEERING 2015年第1期2015卷 1-16页

作者： Thepphakorn, Thatchai Pongcharoen, Pupong Hicks, Chris Pibulsongkram Rajabhat Univ Fac Ind Technol Phitsanulok 65000 Thailand Naresuan Univ Ind Engn Dept CORIA Fac Engn Phitsanulok 65000 Thailand Newcastle Univ Sch Business Newcastle Upon Tyne NE1 7RU Tyne & Wear England

This paper outlines the development of a new evolutionary algorithms based timetabling (EAT) tool for solving course scheduling problems that include a genetic algorithm (GA) and a memetic algorithm (MA). Reproduction processes may generate infeasible solutions. Previous research has used repair processes that have been applied after a population of chromosomes has been generated. This research developed a new approach which (i) modified the genetic operators to prevent the creation of infeasible solutions before chromosomes were added to the population;(ii) included the clonal selection algorithm (CSA);and the elitist strategy (ES) to improve the quality of the solutions produced. This approach was adopted by both the GA and MA within the EAT. The MA was further modified to include hill climbing local search. The EAT program was tested using 14 benchmark timetabling problems from the literature using a sequential experimental design, which included a fractional factorial screening experiment. Experiments were conducted to (i) test the performance of the proposed modified algorithms;(ii) identify which factors and interactions were statistically significant;(iii) identify appropriate parameters for the GA and MA;and (iv) compare the performance of the various hybrid algorithms. The genetic algorithm with modified genetic operators produced an average improvement of over 50%.

关键词： clonal selection algorithms EVOLUTIONARY algorithms TIME perspective MEMETICS FACTOR analysis

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Recent Advances in clonal selection algorithms and Applications

Recent Advances in Clonal Selection Algorithms and Applicati...

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IEEE Symposium Series on Computational Intelligence

作者： Wenjian Luo Xin Lin Anhui Province Key Laboratory of Software Engineering in Computing and Communication University of Science and Technology of China Hefei Anhui China

关键词： Immune Computation clonal selection algorithms Optimization

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A review of clonal selection algorithm and its applications

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ARTIFICIAL INTELLIGENCE REVIEW 2011年第2期36卷 117-138页

作者： Ulutas, Berna Haktanirlar Kulturel-Konak, Sadan Eskisehir Osmangazi Univ Dept Ind Engn TR-26480 Eskisehir Turkey Penn State Berks Management Informat Syst Reading PA 19610 USA

Recently, clonal selection theory in the immune system has received the attention of researchers and given them inspiration to create algorithms that evolve candidate solutions by means of selection, cloning, and mutation procedures. Moreover, diversity in the population is enabled by means of the receptor editing process. The clonal selection Algorithm (CSA) in its canonical form and its various versions are used to solve different types of problems and are reported to perform better compared with other heuristics (i.e., genetic algorithms, neural networks, etc.) in some cases, such as function optimization and pattern recognition. Although the studies related with CSA are increasingly popular, according to our best knowledge, there is no study summarizing the basic features of these algorithms, hybrid algorithms, and the application areas of these algorithms all in one paper. Therefore, this study aims to summarize the powerful characteristics and general review of CSA. In addition, CSA based hybrid algorithms are reviewed, and open research areas are discussed for further research.

关键词： Artificial immune systems clonal selection algorithms Review Hybrid algorithms

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Aligning multiple protein sequences by hybrid clonal selection algorithm with insert-remove-gaps and BlockShuffling operators

Aligning multiple protein sequences by hybrid clonal selecti...

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5th International Conference on Artificial Immune Systems

作者： Cutello, V. Lee, D. Nicosia, G. Pavone, M. Prizzi, I. Univ Catania Dept Math & Comp Sci I-95125 Catania Italy Korea Adv Inst Sci & Technol Dept BioSyst IBM KAIST Biocomp Res Ctr Taejon South Korea Diogenes Res Ctr Catania Italy

ISBN: (纸本)3540377492

Multiple sequence alignment (MSA) is one of the most important tasks in biological sequence analysis. This paper will primarily focus on on protein alignments, but most of the discussion and methodology also applies to DNA alignments. A novel hybrid clonal selection algorihm, called an aligner, is presented. It searches for a set of alignments amongst the population of candidate alignments by optimizing the classical weighted sum of pairs objective function. Benchmarks from BaliBASE library (v.1.0 and v.2.0) are used to validate the algorithm. Experimental results of BaliBASE v.1.0 benchmarks show that the proposed algorithm is superior to PRRP, ClustaIX, SAGA, DIALIGN, PIMA, MULTIALIGN, and PILEUP8. On BaliBASE v.2.0 benchmarks the algorithm shows interesting results in terms of SP score with respect to established and leading methods, i.e. Clustaff, T-Coffee, MUSCLE, PRALINE, ProbCons, and, Spern.

关键词： bioinformatics multiple sequence alignment protein sequences immune algorithms clonal selection algorithms hypermutation operator

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A clonal selection algorithm for coloring, hitting set and satisfiability problems

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16th Italian Workshop on Neural Nets

作者： Cutello, Vincenzo Nicosia, Giuseppe Univ Catania Dept Math & Comp Sci I-95125 Catania Italy

ISBN: (纸本)3540331832

In this keynote paper we present an Immune Algorithm based on the clonal selection Principle to explore the combinatorial optimization capability. We consider only two immunological entities, antigens and B cells, three parameters, and the cloning, hypermutation and aging immune operators. The experimental results shows how these immune operators couple the clonal expansion dynamics are sufficient to obtain optimal solutions for graph coloring problem, minimum hitting set problem and satisfiability hard instances, and that the IA designed is very competitive with the best evolutionary algorithms.

关键词： immune algorithms clonal selection algorithms evolutionary computation information gain combinatorial optimization graph coloring problem minimum hitting set problem satisfiability

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A clonal selection Algorithm for Makespan Minimization on Unrelated Parallel Machines with Sequence Dependent Setup Times

A Clonal Selection Algorithm for Makespan Minimization on Un...

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2nd Brazilian Conference on Intelligent Systems (BRACIS)

作者： Diana, Rodney O. M. Filho, Moacir F. de F. de Souza, Sergio R. Silva, Maria Amelia L. Ctr Fed Educ Tecnol Minas Gerais BR-30510000 Belo Horizonte MG Brazil

ISBN: (纸本)9780769550923

This work presents a proposal to implement the clonal selection algorithm (clonalG) for the makespan minimization problem on unrelated parallel machines with sequence-dependent setup times. The initial population is created through the GRASP algorithm, whereas an evaluation function based on makespan values and on a historical of recent improvements helps the algorithm to escape from local optima. A local search heuristic, based in the VND method, was used as the hypermutation somatic operator, in order to accelerate the convergence of the algorithm. The performed experiments show that the proposed operators improve the performance of standard clonalG algorithm as well as allow to obtain better results than those presented in the most recent literature.

关键词： Scheduling Makespan Artificial immune systems clonal selection algorithms

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An immune-inspired algorithm for an unrelated parallel machines' scheduling problem with sequence and machine dependent setup-times for makespan minimisation

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NEUROCOMPUTING 2015年 163卷 94-105页

作者： Marinho Diana, Rodney Oliveira de Franca Filho, Moacir Felizardo de Souza, Sergio Ricardo de Almeida Vitor, Joao Francisco Fed Ctr Technol Educ Minas Gerais CEFET MG BR-30510000 Belo Horizonte MG Brazil

This paper proposes an immune-inspired algorithm to the problem of minimising the makespan on unrelated parallel machines, with sequence dependent setup times. The initial population is generated through the construction phase of the Greedy Randomised Adaptive Search Procedure (GRASP). An evaluation function is proposed to help the algorithm escape from local optima. A Variable Neighbourhood Descent (VND) local search heuristic, which makes significant use of the characteristics of the problem, is proposed as a somatic hypermutation operator to accelerate the convergence of the algorithm. A population re-selection operator, which strategically keeps good quality solutions with a high level of dispersion in the search space, is also proposed. The experiments performed show that the proposed algorithm enables better results than those reported in recent literature studies. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Scheduling Makespan Artificial immune systems clonal selection algorithms

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