40,987 research outputs found
A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems
Copyright @ Springer-Verlag 2008Dynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This paper investigates the application of memetic algorithms, a class of hybrid evolutionary algorithms, for dynamic optimization problems. An adaptive hill climbing method is proposed as the local search technique in the framework of memetic algorithms, which combines the features of greedy crossover-based hill climbing and steepest mutation-based hill climbing. In order to address the convergence problem, two diversity maintaining methods, called adaptive dual mapping and triggered random immigrants, respectively, are also introduced into the proposed memetic algorithm for dynamic optimization problems. Based on a series of dynamic problems generated from several stationary benchmark problems, experiments are carried out to investigate the performance of the proposed memetic algorithm in comparison with some peer evolutionary algorithms. The experimental results show the efficiency of the proposed memetic algorithm in dynamic environments.This work was supported by the National Nature Science Foundation of China (NSFC) under Grant Nos. 70431003 and 70671020, the National Innovation Research Community Science Foundation of China under Grant No. 60521003, and the National Support Plan of China under Grant No. 2006BAH02A09 and the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01
A multi-agent based evolutionary algorithm in non-stationary environments
This article is posted here with permission of IEEE - Copyright @ 2008 IEEEIn this paper, a multi-agent based evolutionary algorithm (MAEA) is introduced to solve dynamic optimization problems. The agents simulate living organism features and co-evolve to find optimum. All agents live in a lattice like environment, where each agent is fixed on a lattice point. In order to increase the energy, agents can compete with their neighbors and can also acquire knowledge based on statistic information. In order to maintain the diversity of the population, the random immigrants and adaptive primal dual mapping schemes are used. Simulation experiments on a set of dynamic benchmark problems show that MAEA can obtain a better performance in non-stationary environments in comparison with several peer genetic algorithms.This work was suported by the Key Program of National Natural Science Foundation of China under Grant No. 70431003, the Science Fund for Creative Research Group of the National Natural Science Foundation of China under Grant No. 60521003, the National Science and Technology Support Plan of China under Grant No. 2006BAH02A09, and the Engineering and Physical Sciences Research Council of the United Kingdom under Grant No. EP/E060722/1
Hyper-learning for population-based incremental learning in dynamic environments
This article is posted here here with permission from IEEE - Copyright @ 2009 IEEEThe population-based incremental learning (PBIL) algorithm is a combination of evolutionary optimization and competitive learning. Recently, the PBIL algorithm has been applied for dynamic optimization problems. This paper investigates the effect of the learning rate, which is a key parameter of PBIL, on the performance of PBIL in dynamic environments. A hyper-learning scheme is proposed for PBIL, where the learning rate is temporarily raised whenever the environment changes. The hyper-learning scheme can be combined with other approaches, e.g., the restart and hypermutation schemes, for PBIL in dynamic environments. Based on a series of dynamic test problems, experiments are carried out to investigate the effect of different learning rates and the proposed hyper-learning scheme in combination with restart and hypermutation schemes on the performance of PBIL. The experimental results show that the learning rate has a significant impact on the performance of the PBIL algorithm in dynamic environments and that the effect of the proposed hyper-learning scheme depends on the environmental dynamics and other schemes combined in the PBIL algorithm.The work by Shengxiang Yang was supported by the Engineering and Physical Sciences Research Council (EPSRC) of the United Kingdom under Grant EP/E060722/1
Learning behavior in abstract memory schemes for dynamic optimization problems
This is the post-print version of this article. The official article can be accessed from the link below - Copyright @ 2009 Springer VerlagIntegrating memory into evolutionary algorithms is one major approach to enhance their performance in dynamic environments. An abstract memory scheme has been recently developed for evolutionary algorithms in dynamic environments, where the abstraction of good solutions is stored in the memory instead of good solutions themselves to improve future problem solving. This paper further investigates this abstract memory with a focus on understanding the relationship between learning and memory, which is an important but poorly studied issue for evolutionary algorithms in dynamic environments. The experimental study shows that the abstract memory scheme enables learning processes and hence efficiently improves the performance of evolutionary algorithms in dynamic environments.The work by S. Yang was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1
On the performance of a hybrid genetic algorithm in dynamic environments
The ability to track the optimum of dynamic environments is important in many
practical applications. In this paper, the capability of a hybrid genetic
algorithm (HGA) to track the optimum in some dynamic environments is
investigated for different functional dimensions, update frequencies, and
displacement strengths in different types of dynamic environments. Experimental
results are reported by using the HGA and some other existing evolutionary
algorithms in the literature. The results show that the HGA has better
capability to track the dynamic optimum than some other existing algorithms.Comment: This paper has been submitted to Applied Mathematics and Computation
on May 22, 2012 Revised version has been submitted to Applied Mathematics and
Computation on March 1, 201
Multi-population methods with adaptive mutation for multi-modal optimization problems
open access journalThis paper presents an efficient scheme to locate multiple peaks on multi-modal optimization problems by using genetic algorithms (GAs). The premature convergence problem shows due to the loss of diversity, the multi-population technique can be applied to maintain the diversity in the population and the convergence capacity of GAs. The proposed scheme is the combination of multi-population with adaptive mutation operator, which determines two different mutation probabilities for different sites of the solutions. The probabilities are updated by the fitness and distribution of solutions in the search space during the evolution process. The experimental results demonstrate the performance of the proposed algorithm based on a set of benchmark problems in comparison with relevant algorithms
Poliovirus intrahost evolution is required to overcome tissue-specific innate immune responses.
RNA viruses, such as poliovirus, have a great evolutionary capacity, allowing them to quickly adapt and overcome challenges encountered during infection. Here we show that poliovirus infection in immune-competent mice requires adaptation to tissue-specific innate immune microenvironments. The ability of the virus to establish robust infection and virulence correlates with its evolutionary capacity. We further identify a region in the multi-functional poliovirus protein 2B as a hotspot for the accumulation of minor alleles that facilitate a more effective suppression of the interferon response. We propose that population genetic dynamics enables poliovirus spread between tissues through optimization of the genetic composition of low frequency variants, which together cooperate to circumvent tissue-specific challenges. Thus, intrahost virus evolution determines pathogenesis, allowing a dynamic regulation of viral functions required to overcome barriers to infection.RNA viruses, such as polioviruses, have a great evolutionary capacity and can adapt quickly during infection. Here, the authors show that poliovirus infection in mice requires adaptation to innate immune microenvironments encountered in different tissues
A survey of QoS-aware web service composition techniques
Web service composition can be briefly described as the process of aggregating services with disparate functionalities into a new composite service in order to meet increasingly complex needs of users. Service composition process has been accurate on dealing with services having disparate functionalities, however, over the years the number of web services in particular that exhibit similar functionalities and varying Quality of Service (QoS) has significantly increased. As such, the problem becomes how to select appropriate web services such that the QoS of the resulting composite service is maximized or, in some cases, minimized. This constitutes an NP-hard problem as it is complicated and difficult to solve. In this paper, a discussion of concepts of web service composition and a holistic review of current service composition techniques proposed in literature is presented. Our review spans several publications in the field that can serve as a road map for future research
Digital evolution in time-dependent fitness landscapes
We study the response of populations of digital organisms that adapt to a time-varying (periodic) fitness landscape of two oscillating peaks. We corroborate in general predictions from quasi-species theory in dynamic landscapes, such as adaptation to the average fitness landscape at small periods (high frequency) and quasistatic adaptation at large periods (low frequency). We also observe adaptive phase shifts (time tags between a change in the fitness landscape and art adaptive change in the population) that indicate a low-pass filter effect, in agreement with existing theory,. Finally, we witness long-term adaptation to fluctuating environments not anticipated in previous theoretical work
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