Self-adaptation of mutation distribution in evolution strategies for dynamic optimization problems

Copyright @ IOS Press. All Rights Reserved.Evolution strategies with q-Gaussian mutation, which allows the self-adaptation of the mutation distribution shape, is proposed for dynamic optimization problems in this paper. In the proposed method, a real parameter q, which allows to smoothly control the shape of the mutation distribution, is encoded in the chromosome of the individuals and is allowed to evolve. In the experimental study, the q-Gaussian mutation is compared to Gaussian and Cauchy mutation on experiments generated from the simulation of evolutionary robots and on dynamic optimization problems generated by the Moving Peaks generator

Continuous dynamic problem generators for evolutionary algorithms

This article is posted here with permission from IEEE - Copyright @ 2007 IEEEAddressing dynamic optimization problems has attracted a growing interest from the evolutionary algorithm community in recent years due to its importance in the applications of evolutionary algorithms in real world problems. In order to study evolutionary algorithms in dynamic environments, one important work is to develop benchmark dynamic environments. This paper proposes two continuous dynamic problem generators. Both generators use linear transformation to move individuals, which preserves the distance among individuals. In the first generator, the linear transformation of individuals is equivalent to change the direction of some axes of the search space while in the second one it is obtained by successive rotations in different planes. Preliminary experiments were carried out to study the performance of some standard genetic algorithms in continuous dynamic environments created by the proposed generators

Enhancing MB-OFDM throughput with dual circular 32-QAM

Quadrature Phase Shift Keying (QPSK) and Dual Carrier Modulation (DCM) are currently used as the modulation schemes for Multiband Orthogonal Frequency Division Multiplexing (MB-OFDM) in the ECMA-368 defined Ultra-Wideband (UWB) radio platform. ECMA-368 has been chosen as the physical radio platform for many systems including Wireless USB (W-USB), Bluetooth 3.0 and Wireless HDMI; hence ECMA-368 is an important issue to consumer electronics and the users experience of these products. To enable the transport of high-rate USB, ECMA-368 offers up to 480 Mb/s instantaneous bit rate to the Medium Access Control (MAC) layer, but depending on radio channel conditions dropped packets unfortunately result in a lower throughput. This paper presents an alternative high data rate modulation scheme that fits within the configuration of the current standard increasing system throughput by achieving 600 Mb/s (reliable to 3.1 meters) thus maintaining the high rate USB throughput even with a moderate level of dropped packets. The modulation system is termed Dual Circular 32-QAM (DC 32-QAM). The system performance for DC 32-QAM modulation is presented and compared with 16-QAM and DCM1

Hyper-selection in dynamic environments

This article is posted here with permission from IEEE - Copyright @ 2008 IEEEIn recent years, several approaches have been developed for genetic algorithms to enhance their performance in dynamic environments. Among these approaches, one kind of methods is to adapt genetic operators in order for genetic algorithms to adapt to a new environment. This paper investigates the effect of the selection pressure on the performance of genetic algorithms in dynamic environments. A hyper-selection scheme is proposed for genetic algorithms, where the selection pressure is temporarily raised whenever the environment changes. The hyper-selection scheme can be combined with other approaches for genetic algorithms in dynamic environments. Experiments are carried out to investigate the effect of different selection pressures on the performance of genetic algorithms in dynamic environments and to investigate the effect of the hyper-selection scheme on the performance of genetic algorithms in combination with several other schemes in dynamic environments. The experimental results indicate that the effect of the hyper-selection scheme depends on the problem under consideration and other schemes combined in genetic algorithms.This work was supported by UK EPSRC under Grant No. EP/E060722/1 and Brazil FAPESP under Grant Proc. 04/04289-6

Evolution strategies with q-Gaussian mutation for dynamic optimization problems

This article is posted here with permmission from IEEE - Copyright @ 2010 IEEEEvolution strategies with q-Gaussian mutation, which allows the self-adaptation of the mutation distribution shape, is proposed for dynamic optimization problems in this paper. In the proposed method, a real parameter q, which allows to smoothly control the shape of the mutation distribution, is encoded in the chromosome of the individuals and is allowed to evolve. In the experimental study, the q-Gaussian mutation is compared to Gaussian and Cauchy mutation on four experiments generated from the simulation of evolutionary robots.This work was supported by FAPESP, Brazil, and by the Engineering and Physical Sciences Research Council(EP/E060722/1), UK

Self-adaptation of mutation distribution in evolutionary algorithms

This paper is posted here with permission from IEEE - Copyright @ 2007 IEEEThis paper proposes a self-adaptation method to control not only the mutation strength parameter, but also the mutation distribution for evolutionary algorithms. For this purpose, the isotropic g-Gaussian distribution is employed in the mutation operator. The g-Gaussian distribution allows to control the shape of the distribution by setting a real parameter g and can reproduce either finite second moment distributions or infinite second moment distributions. In the proposed method, the real parameter q of the g-Gaussian distribution is encoded in the chromosome of an individual and is allowed to evolve. An evolutionary programming algorithm with the proposed idea is presented. Experiments were carried out to study the performance of the proposed algorithm

Evolutionary programming with q-Gaussian mutation for dynamic optimization problems

This article is posted here with permission from IEEE - Copyright @ 2008 IEEEThe use of evolutionary programming algorithms with self-adaptation of the mutation distribution for dynamic optimization problems is investigated in this paper. In the proposed method, the q-Gaussian distribution is employed to generate new candidate solutions by mutation. A real parameter q, which defines the shape of the distribution, is encoded in the chromosome of individuals and is allowed to evolve. Algorithms with self-adapted mutation generated from isotropic and anisotropic distributions are presented. In the experimental study, the q-Gaussian mutation is compared to Gaussian and Cauchy mutation on three dynamic optimization problems.This work was supported by Brazil FAPESP under Grant 04/04289-6 and UK EPSRC under Grant No. EP/E060722/01