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Genetic and evolutionary Computation Conference (GECCO)

作者： Ben-Romdhane, Hajer Alba, Enrique Krichen, Saoussen Univ Tunis LARODEC Lab ISG Tunis Tunis Tunisia Univ Malaga Malaga Spain

ISBN: (纸本)9781450349390

Striking an effective balance between exploration and exploitation (E&E) is still one of the major concerns when using evolutionary algorithms (EAs) in dynamic environments. In this work, a new scheme for adaptively balancing E&E in EAs is proposed. Based on the results of a statistical Pre-Post analysis of the population, the next search mode can be decided (i.e., exploration or exploitation). The experimental results showed that our proposal excels versus several competing approaches from the state of the art.

关键词： evolutionary algorithms Exploration-exploitation tradeoff Dynamic optimization Pre-Post testing

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An evolutionary approach for self-organization of contract manufacturing supply chains

An evolutionary approach for self-organization of contract m...

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2001 IEEE International Conference on Systems, Man and Cybernetics

作者： Hsieh, Fu-Shiung

Contract manufacturing is one of the most prevalent supply chain models recently. Formation of contract manufacturing supply chain network tends to be more dynamic as establishment of partnership is based on the contracted orders. Optimization of contract manufacturing supply chain networks is much more difficult than traditional production networks. The objective of this paper is to propose a self-organization framework for contract manufacturing supply chain. We define several constraint satisfaction genetic operator to model key activities such as changing upstream or downstream partners or reallocation of production capacities among partners in contract manufacturing supply chains. Our results include: (a)a mathematical model for contract manufacturing supply chain network, (b) a self-organization framework based on evolutionary approach, (c) comparison on different types of supply chain alliance and (d) a constraint satisfaction algorithm.

关键词： evolutionary algorithms

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Flying tourist problem: Flight time and cost minimization in complex routes

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EXPERT SYSTEMS WITH APPLICATIONS 2019年第0期130卷 172-187页

作者： Marques, Rafael Russo, Luis Roma, Nuno Univ Lisbon Inst Super Tecn Lisbon Portugal INESC ID Rua Alves Redol 9 P-1000029 Lisbon Portugal

This work introduces and formalizes the Flying Tourist Problem (FTP), whose goal is to find the best schedule, route, and set of flights for any given unconstrained multi-city flight request. To solve the FTP, the developed work proposes a methodology that allows an efficient resolution of this rather demanding problem. This strategy uses different heuristics and meta-heuristic optimization algorithms, allowing the identification of solutions in real-time, even for large problem instances. The implemented system was evaluated using different criteria, including the provided gains (in terms of total flight price and duration) and its performance compared to other similar systems. The obtained results show that the developed optimization system consistently presents solutions that are up to 35% cheaper (or 60% faster) than those developed by simpler heuristics. Furthermore, when comparing the developed system to the only publicly available (but not-disclosed) alternative for flight search, it was shown that it provides the best-recommended and the cheapest solutions, respectively 74% and 95% of the times, allowing the user to save time and money. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Flight search Traveling salesman problem Combinatorial optimization evolutionary algorithms

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Multi-objective PSO algorithm for mining numerical association rules without a priori discretization

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EXPERT SYSTEMS WITH APPLICATIONS 2014年第9期41卷 4259-4273页

作者： Beiranvand, Vahid Mobasher-Kashani, Mohamad Abu Bakar, Azuraliza Univ British Columbia Sch Arts & Sci IGS Optimizat Vancouver BC V5Z 1M9 Canada Univ Kebangsaan Malaysia Fac Informat Sci & Technol Ctr Artificial Intelligence Technol Bangi 63400 Selangor Malaysia

In the domain of association rules mining (ARM) discovering the rules for numerical attributes is still a challenging issue. Most of the popular approaches for numerical ARM require a priori data discretization to handle the numerical attributes. Moreover, in the process of discovering relations among data, often more than one objective (quality measure) is required, and in most cases, such objectives include conflicting measures. In such a situation, it is recommended to obtain the optimal trade-off between objectives. This paper deals with the numerical ARM problem using a multi-objective perspective by proposing a multi-objective particle swarm optimization algorithm (i.e., MOPAR) for numerical ARM that discovers numerical association rules (ARs) in only one single step. To identify more efficient ARs, several objectives are defined in the proposed multi-objective optimization approach, including confidence, comprehensibility, and interestingness. Finally, by using the Pareto optimality the best ARs are extracted. To deal with numerical attributes, we use rough values containing lower and upper bounds to show the intervals of attributes. In the experimental section of the paper, we analyze the effect of operators used in this study, compare our method to the most popular evolutionary-based proposals for ARM and present an analysis of the mined ARs. The results show that MOPAR extracts reliable (with confidence values close to 95%), comprehensible, and interesting numerical ARs when attaining the optimal trade-off between confidence, comprehensibility and interestingness. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Data mining Multi-objective optimization Association rules evolutionary algorithms

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Baldwinian learning in clonal selection algorithm for optimization

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INFORMATION SCIENCES 2010年第8期180卷 1218-1236页

作者： Gong, Maoguo Jiao, Licheng Zhang, Lining Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ China Inst Intelligent Informat Proc Xian 710071 Peoples R China

Artificial immune systems are a kind of new computational intelligence methods which draw inspiration from the human immune system. Most immune system inspired optimization algorithms are based on the applications of clonal selection and hypermutation, and known as clonal selection algorithms. These clonal selection algorithms simulate the immune response process based on principles of Darwinian evolution by using various forms of hypermutation as variation operators. The generation of new individuals is a form of the trial and error process. It seems very wasteful not to make use of the Baldwin effect in immune system to direct the genotypic changes. In this paper, based on the Baldwin effect. an improved clonal selection algorithm, Baldwinian Clonal Selection Algorithm, termed as BCSA, is proposed to deal with optimization problems. BCSA evolves and improves antibody population by four operators, clonal proliferation, Baldwinian learning, hypermutation, and clonal selection. It is the first time to introduce the Baldwinian learning into artificial immune systems. The Baldwinian learning operator simulates the learning mechanism in immune system by employing information from within the antibody population to alter the search space. It makes use of the exploration performed by the phenotype to facilitate the evolutionary search for good genotypes. In order to validate the effectiveness of BCSA, eight benchmark functions, six rotated functions, six composition functions and a real-world problem, optimal approximation of linear systems are solved by BCSA, successively. Experimental results indicate that BCSA performs very well in solving most of the test problems and is an effective and robust algorithm for optimization. (C) 2009 Elsevier Inc. All rights reserved.

关键词： Baldwin effect Clonal selection Artificial immune systems evolutionary algorithms Optimization

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Hybrid Particle Swarm Optimization with Iterative Local Search for DNA Sequence Assembly

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CURRENT BIOINFORMATICS 2015年第4期10卷 393-400页

作者： Rajagopal, Indumathy Sankareswaran, Uma Maheswari Sri Krishna Arts & Sci Coll Dept Comp Technol Coimbatore 641008 Tamil Nadu India

This paper proposes a novel hybrid approach to solve the DNA sequence assembly problem by combining particle swarm optimization and iterative local search algorithms. One of the vital challenges in DNA sequence assembly is to arrange a long genome sequence that consists of millions of fragments in accurate order. This is an NP- hard combinatorial optimization problem. The prominence of this paper is to demonstrate how this hybrid algorithms scheme can improve the performance of fragment assembly process. Incorporating iterative local search heuristics in particle swarm optimization algorithm efficiently assembles the fragments by maximizing the overlap score. The performances of the proposed hybrid algorithm were compared with the variants of Particle Swarm Optimization algorithms and other known methodologies. The experimental results show that the proposed hybrid approach produces better results than the other techniques when tested with different sized well-known benchmark instances.

关键词： evolutionary algorithms genome sequence assembly inertia weight local search metaheuristics optimization particle swarm optimization smallest position value rule semi-global alignment

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A New Decomposition-Based NSGA-II for Many-Objective Optimization

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2018年第7期48卷 1191-1210页

作者： Elarbi, Maha Bechikh, Slim Gupta, Abhishek Ben Said, Lamjed Ong, Yew-Soon Univ Tunis ISG Tunis High Inst Management Tunis Comp Sci Dept SOIE Lab Tunis 2000 Tunisia Nanyang Technol Univ Sch Comp Engn Computat Intelligence Lab Singapore Singapore Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore SIMTech NTU Joint Lab Complex Syst Singapore 639798 Singapore NTU Rolls Royce Corp Joint Lab Singapore 639798 Singapore

Multiobjective evolutionary algorithms (MOEAs) have proven their effectiveness and efficiency in solving problems with two or three objectives. However, recent studies show that MOEAs face many difficulties when tackling problems involving a larger number of objectives as their behavior becomes similar to a random walk in the search space since most individuals are nondominated with respect to each other. Motivated by the interesting results of decomposition-based approaches and preference-based ones, we propose in this paper a new decomposition-based dominance relation to deal with many-objective optimization problems and a new diversity factor based on the penalty-based boundary intersection method. Our reference point-based dominance (RP-dominance), has the ability to create a strict partial order on the set of nondominated solutions using a set of well-distributed reference points. The RP-dominance is subsequently used to substitute the Pareto dominance in nondominated sorting genetic algorithm-II (NSGA-II). The augmented MOEA, labeled as RP-dominance-based NSGA-II, has been statistically demonstrated to provide competitive and oftentimes better results when compared against four recently proposed decomposition-based MOEAs on commonly-used benchmark problems involving up to 20 objectives. In addition, the efficacy of the algorithm on a realistic water management problem is showcased.

关键词： Decomposition evolutionary algorithms many-objective optimization reference point-based dominance (RP-dominance)

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An Adversarial Approach for Automated Pokemon Team Building and Metagame Balance

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IEEE TRANSACTIONS ON GAMES 2024年第2期16卷 365-375页

作者： Reis, Simao Novais, Rita Reis, Luis Paulo Lau, Nuno Univ Porto Fac Engn Artificial Intelligence & Comp Sci Lab P-4099002 Porto Portugal Univ Aveiro Inst Elect & Informat Engn Aveiro P-3810193 Aveiro Portugal

Metagame balance is a crucial task in game development, and automation of this process could assist game developers by vastly reducing time costs. We explore and evaluate a metagame balance model over the recently proposed VGC AI Competition Framework. We propose an adversarial model where team builder agents try to maximize their win rate by narrowing to the most optimal team configurations, resulting in a reduction of the diversity of Pok & eacute;mon employed, while a balancing agent readapts the Pok & eacute;mon inner attributes to incentivize the team builder agents to incorporate a greater variety of Pok & eacute;mon into their teams increasing the metagame's overall diversity and balance. Furthermore, we develop multiple team builder agents divided into two groups: the first group assumes that individual Pok & eacute;mon advantages are the primary factor to determine the outcome of game matches;the second group also exploits the implicit synergy between teammates. These agents make use of metagaming, linear optimization, and evolutionary search to find strong combinations against the current metagame. The strongest team builder is faced against the team metagame balance agent for its evaluation. Deep learning is also employed to predict the outcome of matches and recommend constructive elements of teams.

关键词： Games Artificial intelligence Genetic algorithms Optimization Deep learning Buildings Training Automatic game design competitive games deep reinforcement learning evolutionary algorithms game balance game theory optimization

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Layout and design optimization of ocean wave energy converters: A scoping review of state-of-the-art canonical, hybrid, cooperative, and combinatorial optimization methods

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ENERGY REPORTS 2022年 8卷 15446-15479页

作者： Golbaz, Danial Asadi, Rojin Amini, Erfan Mehdipour, Hossein Nasiri, Mahdieh Etaati, Bahareh Naeeni, Seyed Taghi Omid Neshat, Mehdi Mirjalili, Seyedali Gandomi, Amir H. Univ Delaware Ctr Appl Coastal Res Civil & Environm Engn Newark DE USA Univ Tehran Sch Civil Engn Coll Engn Tehran 13145 Iran Stevens Inst Technol Dept Civil Environm & Ocean Engn Hoboken NJ 07030 USA Stevens Inst Technol Dept Mech Engn Hoboken NJ 07030 USA Amirkabir Univ Technol Dept Comp Engn & Informat Technol Tehran Iran Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Brisbane Qld 4006 Australia Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia Obuda Univ Univ Res & Innovat Ctr EKIK H-1034 Budapest Hungary

Ocean Wave energy is becoming a prominent technology, which is considered a vital renewable energy resource to achieve the Net-zero Emissions Plan by 2050. It is also projected to be commercialized widely and become a part of the industry that alters conventional energy technologies in the near future. However, wave energy technologies are not entirely yet developed and mature enough, so various criteria must be optimized to enter the energy market. In order to maximize the performance of wave energy converters (WECs) components, three challenges are mostly considered: Geometry, Power Take-off (PTO) parameters, and WECs' layout. As each of such challenges plays a meaningful role in harnessing the maximum power output, this paper systematically reviews applied state-of-the-art optimization techniques, including standard, hybrid, cooperative, bi-level and combinatorial strategies. Due to the importance of fidelity and computational cost in numerical methods, we also discuss approaches to analyzing WECs interactions' developments. Moreover, the benefits and drawbacks of the popular optimization methods applied to improve WEC parameters' performance are summarized, briefly discussing their key characteristics. According to the scoping review, using a combination of bio-inspired algorithms and local search as a hybrid algorithm can outperform the other techniques in layout optimization in terms of convergence rate. A review of the geometry of WECs has emphasized the indispensability of optimizing and balancing design parameters with cost issues in multimodal and large-scale problems. (c) 2022 The Authors. Published by Elsevier Ltd.

关键词： Wave Energy Converters Layout optimization PTO systems Geometry design Optimization algorithms evolutionary algorithms Swarm intelligence Local search methods

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A tribe competition-based genetic algorithm for feature selection in pattern classification

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APPLIED SOFT COMPUTING 2017年 58卷 328-338页

作者： Ma, Benteng Xia, Yong Northwestern Polytech Univ Sch Comp Sci Shaanxi Key Lab Speech & Image Informat Proc SAII Xian 710072 Peoples R China Northwestern Polytech Univ Sch Comp Sci CMCC Xian 710072 Peoples R China

Feature selection has always been a critical step in pattern recognition, in which evolutionary algorithms, such as the genetic algorithm (GA), are most commonly used. However, the individual encoding scheme used in various GAs would either pose a bias on the solution or require a pre-specified number of features, and hence may lead to less accurate results. In this paper, a tribe competition-based genetic algorithm (TCbGA) is proposed for feature selection in pattern classification. The population of individuals is divided into multiple tribes, and the initialization and evolutionary operations are modified to ensure that the number of selected features in each tribe follows a Gaussian distribution. Thus each tribe focuses on exploring a specific part of the solution space. Meanwhile, tribe competition is introduced to the evolution process, which allows the winning tribes, which produce better individuals, to enlarge their sizes, i.e. having more individuals to search their parts of the solution space. This algorithm, therefore, avoids the bias on solutions and requirement of a pre-specified number of features. We have evaluated our algorithm against several state-of-the-art feature selection approaches on 20 benchmark datasets. Our results suggest that the proposed TCbGA algorithm can identify the optimal feature subset more effectively and produce more accurate pattern classification. (C) 2017 Published by Elsevier B.V All rights reserved.

关键词： Feature selection Pattern classification evolutionary algorithms Genetic algorithm Tribe competition

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