Aboubakar Hameed

I am a PhD student in Computer Science at Heriot Watt University.


Email:     hameed_200878@yahoo.com     OR    aah30@hw.ac.uk

Address
Department of Computer Science
School of Mathematics and Computer Science
Heriot-Watt University
Riccarton Campus
Edinburgh, EH14 4AS
United Kingdom

Project title: Large-Scale Optimization: are Co-operative Coevolution and Fitness Inheritance Additive?

Supervisor: Professor David W. Corne
  • Prof. David W. Corne


  • Abstract— Large-scale optimization - here referring mainly to problems with many design parameters - remains a serious challenge for optimization algorithms. When the problem at hand does not succumb to analytical treatment (an overwhelmingly commonplace situation), the engineering and adaptation of stochastic black box optimization methods tends to be a favoured approach, particularly the use of Evolutionary Algorithms (EAs). In this context, many approaches are currently under investigation for accelerating performance on large-scale problems, and we focus on two of those in this research The first is co-operative co-evolution (CC), where the strategy is to successively optimize only subsets of the design parameters at a time, keeping the remainder fixed, with an organized approach to managing and reconciling these ‘subspace’ optimizations. The second is fitness inheritance (FI), which is essentially a very simple surrogate model strategy, in which, with some probability, the fitness of a solution is simply guessed to be a simple function of the fitnesses of that solution’s ‘parents’. Both CC and FI have been found successful on nontrivial and multiple test cases, and they use fundamentally distinct strategies. In this article we explore the extent to which employing both of these strategies at once provides additional benefit.

    WORK DONE:
    1-We combined CC and FI into a straightforward algorithm that we call CCEA-FI,and evaluate them on A set of test functions as in our paper.
    2-we used self-adaptation differential evolution with neighbourhood search (SaNSDE) as the optimizer in each subcomponent instated of basic EAs

    CURRENT WORK:
    At the moment we trying to improve our algorithm further by using self-adaptive sub component size rather than keep it constant during the evaluation processor then will test it on CEC’2010 Functions.

    PUBLICATION:
    Hameed, A.; Corne, D.; Morgan, D.; Waldock, A., "Large-scale optimization: Are co-operative co-evolution and fitness inheritance additive?" Computational Intelligence (UKCI), 2013 13th UK Workshop on, vol., no., pp.104, 111, 9-11 Sept. 2013.