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memetic and Opposition-Based Learning Genetic algorithms for Sorting Unsigned Genomes by Translocations 7th

7th World Congress on Nature and Biologically Inspired Computing (NaBIC)

作者： da Silveira, Lucas A. Soncco-Alvarez, Jose L. de Lima, Thaynara A. Ayala-Rincon, Mauricio Univ Brasilia Dept Comp Sci Brasilia DF Brazil Univ Brasilia Dept Math Brasilia DF Brazil Univ Goias Campus II Dept Math Jatai Go Brazil

ISBN: (纸本)9783319274003;9783319273990

A standard genetic algorithm (GA(S)) for sorting unsigned genomes by translocations is improved in two different manners: firstly, a memetic algorithm (GA(M)) is provided, which embeds a newstage of local search, based on the concept of mutation applied in only one gene;secondly, an opposition-based learning (GA(OBL)) mechanism is provided that explores the concept of internal opposition applied to a chromosome. Both approaches include a convergence control mechanism of the population using the Shannon entropy. For the experiments, both biological and synthetic genomes were used. The results showed that GA(M) outperforms both GA(S) and GA(OBL) as confirmed through statistical tests.

关键词： Sorting permutations Sorting unsigned genomes Genetic algorithms memetic algorithms Opposition-based learning algorithms

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Hybrid evolutionary algorithms for constraint satisfaction problems: memetic overkill?

Hybrid evolutionary algorithms for constraint satisfaction p...

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IEEE Congress on Evolutionary Computation

作者： Craenen, BGW Eiben, AE Napier Univ Edinburgh EH10 5DT Midlothian Scotland

ISBN: (纸本)0780393635

We study a selected group of hybrid EAs for solving CSPs, consisting of the best performing EAs from the literature. We investigate the contribution of the evolutionary component to their performance by comparing the hybrid EAs with their "de-evolutionarised" variants. The experiments show that "de-evolutionarising" can increase performance, in some cases doubling it. Considering that the problem domain and the algorithms are arbitrarily selected from the "memetic niche", it seems likely that the same effect occurs for other problems and algorithms. Therefore, our conclusion is that after designing and building a memetic algorithm, one should perform a verification by comparing this algorithm with its "de-evolutionarised" variant.

关键词： computability computational complexity constraint theory evolutionary computation knowledge verification constraint satisfaction problems de-evolutionarisation hybrid evolutionary algorithms memetic algorithms

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A STUDY ON MEME PROPAGATION IN MULTImemetic algorithms

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INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE 2015年第3期25卷 499-512页

作者： Nogueras, Rafael Cotta, Carlos Univ Malaga Higher Tech Sch Comp Engn Dept Comp Sci & Programming Languages E-29071 Malaga Spain

Multimemetic algorithms (MMAs) are a subclass of memetic algorithms in which memes are explicitly attached to genotypes and evolve alongside them. We analyze the propagation of memes in MMAs with a spatial structure. For this purpose we propose an idealized selecto-Lamarckian model that only features selection and local improvement, and study under which conditions good, high-potential memes can proliferate. We compare population models with panmictic and toroidal grid topologies. We show that the increased takeover time induced by the latter is essential for improving the chances for good memes to express themselves in the population by improving their hosts, hence enhancing their survival rates. Experiments realized with an actual MMA on three different complex pseudo-Boolean functions are consistent with these findings, indicating that memes are more successful in a spatially structured MMA, rather than in a panmictic MMA, and that the performance of the former is significantly better than that of its panmictic counterpart.

关键词： memetic algorithms spatial structure meme propagation

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Studying self-balancing strategies in island-based multimemetic algorithms

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2016年 293卷 180-191页

作者： Nogueras, Rafael Cotta, Carlos Univ Malaga Dept Lenguajes & Ciencias Computac ETSI Informat E-29071 Malaga Spain

Multimemetic algorithms (MMAs) are memetic algorithms that explicitly exploit the evolution of memes, i.e., non-genetic expressions of problem-solving strategies. We aim to study their deployment on an unstable environment with complex topology and volatile resources. We analyze their behavior and performance on environments with different churn rates, and how they are affected by the use of self-balancing strategies aiming to compensate the loss of existing islands and react to the apparition of new ones. We investigate two such strategies, one based on quantitative balance (in which populations are resized dynamically to cope with node failure/recoveries) and another on qualitative balance (in which genetic/memetic information is actually exchanged to achieve balance). We evaluate these on scale-free network topologies and compare them to an unbalanced strategy that keeps island sizes constant. Experimentation firstly focuses on memetic takeover, carried out on an idealized selecto-Lamarckian model of MMAs (used as a surrogate of the latter) and indicating that the two balancing strategies exhibit complementary profiles in terms of diversity preservation. The results also indicate that the qualitative version is more robust to churn than both the unbalanced and the quantitatively balanced counterpart. This is subsequently confirmed with an empirical evaluation of full-fledged MMAs on a benchmark composed of four hard pseudo-Boolean problems. The qualitative version provides the best performance in global terms, significantly outperforming the remaining variants. (C) 2015 Elsevier B.V. All rights reserved.

关键词： memetic algorithms Multimemetic algorithms Load balancing Self-adaptation Faulty environment

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memetic coral reefs optimization algorithms for optimal geometrical design of submerged arches

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SWARM AND EVOLUTIONARY COMPUTATION 2021年 67卷 100958-100958页

作者： Perez-Aracil, J. Camacho-Gomez, C. Hernandez-Diaz, A. M. Pereira, E. Camacho, D. Salcedo-Sanz, S. Univ Alcala Dept Signal Proc & Commun Alcala De Henares Spain Univ Politecn Madrid Dept Comp Syst Engn Madrid Spain Univ La Laguna Continuum Mech & Struct Anal San Cristobal la Laguna Spain

This paper deals with the geometrically nonlinear analysis of submerged arches by means of memetic Coral Reefs Optimization algorithms. The classic design of submerged arches is only focused on calculating the bend-ing stress-less shape (funicular shape) of the structure. Nevertheless, recent works show that this funicular shape can be approached by using a parametric family curve, which also allows a multi-variable optimization of the arch's geometry. Using this novel parametric set of curves, we propose a new Coral Reefs Optimization (CRO) algorithm based on a memetic approach to tackle the geometrically nonlinear design of submerged arches. Specif-ically, the proposed CRO approaches have been tested with different search procedures as exploration operators, and we also test a multi-method version of the algorithm, the Coral Reefs Optimization with Substrate Layers (CRO-SL), which considers several search procedures within the same evolutionary population. A local search to improve the solutions has been considered in all cases, to obtain powerful memetic operators for this problem. It is also shown how the different memetic versions of the CRO (specially those involving multi-methods and Dif-ferential Evolution search procedures), together with the parametric encoding, are able to obtain nearly-optimal geometries for underwater installations. The performance of the proposed algorithm has been compared with state-of-the-art algorithms for optimization: L-SHADE and HCLPSO. Statistical tests have carried out with the aim of comparing the results. It is shown that there is not significant differences between the proposed results by the three algorithms.

关键词： Submerged arches Nonlinear analysis Evolutionary algorithms CRO-SL memetic algorithms

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Improving transit network resilience against disruptions through path redundancy

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Socio-Economic Planning Sciences 2025年 100卷

作者： Iliopoulou, Christina Makridis, Michail A. Kouvelas, Anastasios Institute for Traffic Planning and Transport Systems ETH Zurich Stefano Franscini Square 5 Zurich 8093 Switzerland

Public transport systems are vulnerable to natural disasters, accidents, or deliberate attacks, that can cause infrastructure damage and service disruptions. Disruption impacts depend on the network structure and the availability of alternative travel paths, highlighting the importance of path redundancy in public transport network planning. Addressing the associated research gap, we propose a practice-oriented path redundancy indicator and integrate it within a novel hybrid metaheuristic solution framework to design more resilient route structures from scratch. The approach combines Reinforcement Learning, Local Search operators and Particle Swarm Optimization and is validated using an established benchmark from the literature and a real-world network from Uruguay, generating more resilient networks that serve up to 25 % and 40 % more passengers in random and targeted attacks, respectively. Results show that resilience against link failures can be enhanced through path redundancy without adversely impacting average travel times. © 2025 The Authors

关键词： memetic algorithms Redundancy Reinforcement learning Resilience Transit Route Network design Vulnerability

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memetic micro-genetic algorithms for cancer data classification

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INTELLIGENT SYSTEMS WITH APPLICATIONS 2023年 17卷

作者： Rojas, Matias Gabriel Olivera, Ana Carolina Carballido, Jessica Andrea Vidal, Pablo Javier Univ Nacl Cuyo Inst Univ Tecnol Informac & Comunicac Consejo Nacl Invest Cient & Tecn Padre Jorge Contreras 1300M5502JMS Mendoza Argentina Univ Nacl Consejo Nacl Invest Cient & Tecn Inst Ciencias Ingn Comp San Andres 800 RA-8000 Buenos Aires Argentina Univ Nacl Cuyo Fac Ingn Ctr Univ M5502JMA Mendoza Argentina

Fast and precise medical diagnosis of human cancer is crucial for treatment decisions. Gene selection consists of identifying a set of informative genes from microarray data to allow high predictive accuracy in human cancer classification. This task is a combinatorial search problem, and optimisation methods can be applied for its resolution. In this paper, two memetic micro-genetic algorithms (M mu V1 and M mu V2) with different hybridisation approaches are proposed for feature selection of cancer microarray data. Seven gene expression datasets are used for experimentation. The comparison with stochastic state-of-the-art optimisation techniques concludes that problem-dependent local search methods combined with micro-genetic algorithms improve feature selection of cancer microarray data.

关键词： memetic algorithms Cancer microarray data Micro-genetic algorithm Feature selection

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A learning-based memetic algorithm for the multiple vehicle pickup and delivery problem with LIFO loading

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COMPUTERS & INDUSTRIAL ENGINEERING 2020年 142卷 106241-106241页

作者： Peng, Bo Zhang, Yuan Lu, Zhipeng Cheng, T. C. E. Glover, Fred Southwestern Univ Finance & Econ Sch Business Adm Chengdu 610074 Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol SMART Wuhan 430074 Peoples R China Hong Kong Polytech Univ Dept Logist & Maritime Studies Hung Hom Kowloon Hong Kong Peoples R China Univ Colorado Coll Engn & Appl Sci ECEB Boulder CO 80309 USA

The multiple vehicle pickup and delivery problem is a generalization of the traveling salesman problem that has many important applications in supply chain logistics. One of the most prominent variants requires the route durations and the capacity of each vehicle to lie within given limits, while performing the loading and unloading operations by a last-in-first-out (LIFO) protocol. We propose a learning-based memetic algorithm to solve this problem that incorporates a hybrid initial solution construction method, a learning-based local search procedure, an effective component-based crossover operator utilizing the concept of structured combinations, and a longestcommon-subsequence-based population updating strategy. Experimental results show that our approach is highly effective in terms of both computational efficiency and solution quality in comparison with the current state-of-the-art, improving the previous best-known results for 132 out of 158 problem instances, while matching the best-known results for all but three of the remaining instances.

关键词： Pickup and delivery problem Routing Traveling salesman memetic algorithms Hybrid heuristics Learning mechanisms

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A memetic algorithm for optimal dynamic design of wireless sensor networks

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COMPUTER COMMUNICATIONS 2010年第2期33卷 250-258页

作者： Ferentinos, Konstantinos P. Tsiligiridis, Theodore A. Agr Univ Athens Informat Lab Athens Greece

We present a memetic algorithm that dynamically optimizes the design of a wireless sensor network towards energy conservation and extension of the life span of the network, taking into consideration application-specific requirements, communication constraints and energy consumption of operation and communication tasks of the sensors. The memetic algorithm modifies an already successful genetic algorithm design system and manages to improve its performance. The obtained optimal sensor network designs satisfy all application-specific requirements, fulfill the existing connectivity constraints and incorporate energy conservation characteristics stronger than those of the original genetic algorithm system. Energy management is optimized to guarantee maximum life span of the network without lack of the network characteristics that are required by the specific sensing application. (C) 2009 Elsevier B.V. All rights reserved.

关键词： memetic algorithms Wireless sensor networks Design optimization Energy conservation

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A memetic algorithm for the flexible flow line scheduling problem with processor blocking

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COMPUTERS & OPERATIONS RESEARCH 2009年第2期36卷 402-414页

作者： Tavakkoli-Moghaddam, Reza Safaei, Nima Sassani, Farrokh Univ Tehran Fac Engn Dept Ind Engn Tehran Iran Univ Tehran Fac Engn Engn Optimizat Res Grp Tehran Iran Univ British Columbia Dept Mech Engn Vancouver BC V6T 1Z4 Canada Univ Toronto Dept Mech & Ind Engn Toronto ON M5S 3G8 Canada

This paper introduces an efficient memetic algorithm (MA) combined with a novel local search engine, namely, nested variable neighbourhood search (NVNS), to solve the flexible flow line scheduling problem with processor blocking (FFLB) and without intermediate buffers. A flexible flow line consists of several processing stages in series, with or without intermediate buffers, with each stage having one or more identical parallel processors. The line produces a number of different products, and each product must be processed by at most one processor in each stage. To obtain an optimal solution for this type of complex, large-sized problem in reasonable computational time using traditional approaches and optimization tools is extremely difficult. Our proposed MA employs a new representation, operators, and local search method to solve the above-mentioned problem. The computational results obtained in experiments demonstrate the efficiency of the proposed MA, which is significantly superior to the classical genetic algorithm (CGA) under the same conditions when the population size is increased in the CGA. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： Flexible flow line Processor blocking memetic algorithms Variable neighbourhood search

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