Efficient and Accurate Parallel Genetic Algorithms

Cantú‐Paz, Erick

doi:10.1007/978-1-4615-4369-5

Cited by 517 publications

(466 citation statements)

References 0 publications

Supporting

Mentioning

436

Contrasting

Unclassified

Order By: Relevance

“…They are an advantageous tool for solving difficult global optimization problems, especially in case of problems with many local extrema (see e.g. [1]). The Hierarchical Genetic Strategy (HGS) introduced by Ko lodziej and Schaefer [2] constitutes one of their instances.…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent System for Irregular Parallel Genetic Computations

Momot

Kosacki

Grochowski

et al. 2004

Computational Science - ICCS 2004

View full text Add to dashboard Cite

Abstract. The paper presents the multi-agent, parallel computing system (MAS) composed of a platform of software servers and a set of computing agents. The generic actions of agents and the system government are so designed that it can perform irregular concurrent genetic computations in heterogeneous computer network with a number of computation nodes and connection topology varying in time. The effectiveness of MAS solution is discussed in terms of average migration and communication overheads. Additionally, the MAS system with autonomous, diffusionbased scheduling is compared with low-level distributed implementation, which utilizes the centralized greedy scheduling algorithm.

show abstract

Section: Introductionmentioning

confidence: 99%

Multi-Agent System for Irregular Parallel Genetic Computations

Momot

Kosacki

Grochowski

et al. 2004

Computational Science - ICCS 2004

View full text Add to dashboard Cite

show abstract

“…SPEA2 is O(N 3 ) and NSGA-II is O(mN 2 ), where N is the population size and m is the number of objectives. Our islands are suited for a ring topology [5]. Experiments with other topologies are left for future study.…”

Section: Pmoeamentioning

confidence: 99%

Parallel and Distributed Optimization of Dynamic Data Structures for Multimedia Embedded Systems

Risco‐Martín

Atienza

Hidalgo

et al. 2010

Studies in Computational Intelligence

View full text Add to dashboard Cite

Abstract.Energy-efficient design of multimedia embedded systems demands optimizations in both hardware and software. Software optimization has no received much attention, although modern multimedia applications exhibit high resource utilization. In order to efficiently run this kind of applications in embedded systems, the dynamic memory subsystem needs to be optimized. A key role in this optimization is played by the Dynamic Data Types (DDTs) that reside in every reallife application. It would be desirable to organize this set of DDTs to achieve the best performance in the target embedded system. This problem is NP-complete, and cannot be fully explored. In these cases the use of parallel processing can be very useful because it allows not only to explore more solutions spending the same time, but also to implement new algorithms. In this work, we propose a method that uses parallel processing and evolutionary computation to explore DDTs in the design of embedded applications. We propose a parallel Multi-Objective Evolutionary Algorithm (MOEA) which combines NSGA-II and SPEA2. We use Discrete Event Systems Specification (DEVS) to implement this parallel evolutionary algorithm over Service Oriented Architecture (SOA). Parallelism improves the solutions found by the corresponding sequential algorithms, and it allows system designers to reach better solutions than previous approximations.

show abstract

“…The increase in the computational resources of computers and the increasing number of parallel implementations [4] have lead this growth, making them more appealing for practitioners focused on solving particular problems, rather than theoretical research of the algorithms themselves. There are, however, a number of issues for these applications.…”

Section: Introductionmentioning

confidence: 99%

Mutagenesis as a Diversity Enhancer and Preserver in Evolution Strategies

Guerrero

Gómez-Jordana

Berlanga

et al. 2012

Advances in Intelligent and Soft Computing

View full text Add to dashboard Cite

Mutagenesis is a process which forces the coverage of certain zones of the search space during the generations of an evolution strategy, by keeping track of the covered ranges for the different variables in the so called gene matrix. Originally introduced as an artifact to control the automated stopping criterion in a memetic algorithm, ESLAT, it also improved the exploration capabilities of the algorithm, even though this was considered a secondary matter and not properly analyzed or tested. This work focuses on this diversity enhancement, redefining mutagenesis to increase this characteristic, measuring this improvement over a set of twenty-seven unconstrained optimization functions to provide statistically significant results.

show abstract

Efficient and Accurate Parallel Genetic Algorithms

Cited by 517 publications

References 0 publications

Multi-Agent System for Irregular Parallel Genetic Computations

Multi-Agent System for Irregular Parallel Genetic Computations

Parallel and Distributed Optimization of Dynamic Data Structures for Multimedia Embedded Systems

Mutagenesis as a Diversity Enhancer and Preserver in Evolution Strategies

Contact Info

Product

Resources

About