Combination of Evolutionary Algorithms with Experimental Design, Traditional Optimization and Machine Learning

Evolutionary algorithms alone cannot solve optimization problems very efficiently since there are many random (not very rational) decisions in these algorithms. Combination of evolutionary algorithms and other techniques have been proven to be an efficient optimization methodology. In this talk, I will explain the basic ideas of our three algorithms along this line (1): Orthogonal genetic algorithm which treats crossover/mutation as an experimental design problem, (2) Multiobjective evolutionary algorithm based on decomposition (MOEA/D) which uses decomposition techniques from traditional mathematical programming in multiobjective optimization evolutionary algorithm, and (3) Regular model based multiobjective estimation of distribution algorithms (RM-MEDA) which uses the regular property and machine learning methods for improving multiobjective evolutionary algorithms

A study of the effects of clustering and local search on radio network design: evolutionary computation approaches

Eighth International Conference on Hybrid Intelligent Systems. Barcelona, 10-12 September 2008The goal of this paper is twofold. First, we want to make a study about how evolutionary computation techniques can efficiently solve the radio network design problem. For this goal we test several evolutionary computation techniques within the OPLINK experimental framework and compare them. Second, we propose a clustering approach and a 2-OPT in order to improve the results obtained by the evolutionary algorithms. Experiments carried out provide empirical evidence of how clustering-based techniques help in improving all algorithms tested. Extensive computational tests, including ones without clustering and 2-OPT, are performed with three evolutionary algorithms: genetic algorithms, memetic algorithms and chromosome appearance probability matrix algorithms.Publicad

A Study For Efficiently Solving Optimisation Problems With An Increasing Number Of Design Variables

Coupling optimisation algorithms to Finite Element Methods (FEM) is a very promising way to achieve optimal metal forming processes. However, many optimisation algorithms exist and it is not clear which of these algorithms to use. This paper investigates the sensitivity of a Sequential Approximate Optimisation algorithm (SAO) proposed in [1-4] to an increasing number of design variables and compares it with two other algorithms: an Evolutionary Strategy (ES) and an Evolutionary version of the SAO (ESAO). In addition, it observes the influence of different Designs Of Experiments used with the SAO. It is concluded that the SAO is very capable and efficient and its combination with an ES is not beneficial. Moreover, the use of SAO with Fractional Factorial Design is the most efficient method, rather than Full Factorial Design as proposed in [1-4]

On comparison of constrained and unconstrained evolutions in analogue electronics on the example of “LC” low-pass filters

The Evolutionary Electronics refers to the design method of electronic circuits with the help of Evolutionary Algorithms. Over the years huge experience has been accumulated and tremendous results have been achieved in this field. Two obvious tendencies are prevailing in the area over designers to improve the performance of Evolutionary Algorithms. First of all, as with any solution-search-algorithm, the designers try to reduce the potential solution space in order to reach the optimum solution faster, putting some constrains onto search algorithm as well as onto potential solutions. At the same time, the second tendency of unconstraining the Evolutionary Algorithms in its search gives unpredictable breakthroughs in results. Enabling the evolution to optimize with more experimental parameters devoted to drive the evolution and adjusted previously manually, is one of an example where such kind of unconstraining takes place. The evolution with the maximum freedom of search can be addressed as unconstrained evolution. The unconstrained evolution has already been applied in the past towards the design of digital circuits, and extraordinary results have been obtained, including generation of circuits with smaller number of electronic components. Recently, the similar method has been introduced by authors of this paper towards the design of analogue circuits. The new algorithm has produced promising results in terms of quality of the circuits evolved and evolutionary resources required. It differed from constrained method by its simplicity and represented one of the first attempts to apply Evolutionary Strategy towards the analogue circuit design. In this paper both conventional constrained evolution and newly developed unconstrained evolution in analogue domain are compared in detail on the example of "LC" low-pass filter design. The unconstrained evolution demonstrates the superior behaviour over the constrained one and exceeds by quality of results the best filter evolved previously by 240%. The experimental results are presented along with detailed analysis. Also, the obtained results are compared in details with low-pass filters designed previously

Open-ended evolution to discover analogue circuits for beyond conventional applications

This is the author's accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/s10710-012-9163-8. Copyright @ Springer 2012.Analogue circuits synthesised by means of open-ended evolutionary algorithms often have unconventional designs. However, these circuits are typically highly compact, and the general nature of the evolutionary search methodology allows such designs to be used in many applications. Previous work on the evolutionary design of analogue circuits has focused on circuits that lie well within analogue application domain. In contrast, our paper considers the evolution of analogue circuits that are usually synthesised in digital logic. We have developed four computational circuits, two voltage distributor circuits and a time interval metre circuit. The approach, despite its simplicity, succeeds over the design tasks owing to the employment of substructure reuse and incremental evolution. Our findings expand the range of applications that are considered suitable for evolutionary electronics

Network science algorithms for mobile networks.

Network Science is one of the important and emerging fields in computer science and engineering that focuses on the study and analysis of different types of networks. The goal of this dissertation is to design and develop network science algorithms that can be used to study and analyze mobile networks. This can provide essential information and knowledge that can help mobile networks service providers to enhance the quality of the mobile services. We focus in this dissertation on the design and analysis of different network science techniques that can be used to analyze the dynamics of mobile networks. These techniques include evolutionary clustering, classification, discovery of maximal cliques, and evolutionary centrality algorithms. We proposed evolutionary clustering and evolutionary centrality algorithms that can be used to dynamically discover clusters and central nodes in mobile networks. Overall, the experimental results show that the proposed evolutionary algorithms are robust to short-term variations but reflects long-term trends and can be used effectively to analyze the dynamics of mobile networks