Synthesis of time-to-amplitude converter by mean coevolution with adaptive parameters

Copyright © 2011 the authors and Scientific Research Publishing Inc. This work is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)The challenging task to synthesize automatically a time-to-amplitude converter, which unites by its functionality several digital circuits, has been successfully solved with the help of a novel methodology. The proposed approach is based on a paradigm according to which the substructures are regarded as additional mutation types and when ranged with other mutations form a new adaptive individual-level mutation technique. This mutation approach led to the discovery of an original coevolution strategy that is characterized by very low selection rates. Parallel island-model evolution has been running in a hybrid competitive-cooperative interaction throughout two incremental stages. The adaptive population size is applied for synchronization of the parallel evolutions

A directed mutation operator for real coded genetic algorithms

Copyright @ Springer-Verlag Berlin Heidelberg 2010.Developing directed mutation methods has been an interesting research topic to improve the performance of genetic algorithms (GAs) for function optimization. This paper introduces a directed mutation (DM) operator for GAs to explore promising areas in the search space. In this DM method, the statistics information regarding the fitness and distribution of individuals over intervals of each dimension is calculated according to the current population and is used to guide the mutation of an individual toward the neighboring interval that has the best statistics result in each dimension. Experiments are carried out to compare the proposed DM technique with an existing directed variation on a set of benchmark test problems. The experimental results show that the proposed DM operator achieves a better performance than the directed variation on most test problems

A comparative study of adaptive mutation operators for metaheuristics

Genetic algorithms (GAs) are a class of stochastic optimization methods inspired by the principles of natural evolution. Adaptation of strategy parameters and genetic operators has become an important and promising research area in GAs. Many researchers are applying adaptive techniques to guide the search of GAs toward optimum solutions. Mutation is a key component of GAs. It is a variation operator to create diversity for GAs. This paper investigates several adaptive mutation operators, including population level adaptive mutation operators and gene level adaptive mutation operators, for GAs and compares their performance based on a set of uni-modal and multi-modal benchmark problems. The experimental results show that the gene level adaptive mutation operators are usually more efficient than the population level adaptive mutation operators for GAs

A self-learning particle swarm optimizer for global optimization problems

Copyright @ 2011 IEEE. All Rights Reserved. This article was made available through the Brunel Open Access Publishing Fund.Particle swarm optimization (PSO) has been shown as an effective tool for solving global optimization problems. So far, most PSO algorithms use a single learning pattern for all particles, which means that all particles in a swarm use the same strategy. This monotonic learning pattern may cause the lack of intelligence for a particular particle, which makes it unable to deal with different complex situations. This paper presents a novel algorithm, called self-learning particle swarm optimizer (SLPSO), for global optimization problems. In SLPSO, each particle has a set of four strategies to cope with different situations in the search space. The cooperation of the four strategies is implemented by an adaptive learning framework at the individual level, which can enable a particle to choose the optimal strategy according to its own local fitness landscape. The experimental study on a set of 45 test functions and two real-world problems show that SLPSO has a superior performance in comparison with several other peer algorithms.This work was supported by the Engineering and Physical Sciences Research Council of U.K. under Grants EP/E060722/1 and EP/E060722/2

Adaptive Investment Strategies For Periodic Environments

In this paper, we present an adaptive investment strategy for environments with periodic returns on investment. In our approach, we consider an investment model where the agent decides at every time step the proportion of wealth to invest in a risky asset, keeping the rest of the budget in a risk-free asset. Every investment is evaluated in the market via a stylized return on investment function (RoI), which is modeled by a stochastic process with unknown periodicities and levels of noise. For comparison reasons, we present two reference strategies which represent the case of agents with zero-knowledge and complete-knowledge of the dynamics of the returns. We consider also an investment strategy based on technical analysis to forecast the next return by fitting a trend line to previous received returns. To account for the performance of the different strategies, we perform some computer experiments to calculate the average budget that can be obtained with them over a certain number of time steps. To assure for fair comparisons, we first tune the parameters of each strategy. Afterwards, we compare the performance of these strategies for RoIs with different periodicities and levels of noise.Comment: Paper submitted to Advances in Complex Systems (November, 2007) 22 pages, 9 figure

Particle swarm optimization with composite particles in dynamic environments

This article is placed here with the permission of IEEE - Copyright @ 2010 IEEEIn recent years, there has been a growing interest in the study of particle swarm optimization (PSO) in dynamic environments. This paper presents a new PSO model, called PSO with composite particles (PSO-CP), to address dynamic optimization problems. PSO-CP partitions the swarm into a set of composite particles based on their similarity using a "worst first" principle. Inspired by the composite particle phenomenon in physics, the elementary members in each composite particle interact via a velocity-anisotropic reflection scheme to integrate valuable information for effectively and rapidly finding the promising optima in the search space. Each composite particle maintains the diversity by a scattering operator. In addition, an integral movement strategy is introduced to promote the swarm diversity. Experiments on a typical dynamic test benchmark problem provide a guideline for setting the involved parameters and show that PSO-CP is efficient in comparison with several state-of-the-art PSO algorithms for dynamic optimization problems.This work was supported in part by the Key Program of the National Natural Science Foundation (NNSF) of China under Grant 70931001 and 70771021, the Science Fund for Creative Research Group of the NNSF of China under Grant 60821063 and 70721001, the Ph.D. Programs Foundation of the Ministry of education of China under Grant 200801450008, and by the Engineering and Physical Sciences Research Council of U.K. under Grant EP/E060722/1

Tracking moving optima using Kalman-based predictions

The dynamic optimization problem concerns finding an optimum in a changing environment. In the field of evolutionary algorithms, this implies dealing with a timechanging fitness landscape. In this paper we compare different techniques for integrating motion information into an evolutionary algorithm, in the case it has to follow a time-changing optimum, under the assumption that the changes follow a nonrandom law. Such a law can be estimated in order to improve the optimum tracking capabilities of the algorithm. In particular, we will focus on first order dynamical laws to track moving objects. A vision-based tracking robotic application is used as testbed for experimental comparison