Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
The grid
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How to solve it: modern heuristics
Application-level scheduling on distributed heterogeneous networks
Supercomputing '96 Proceedings of the 1996 ACM/IEEE conference on Supercomputing
Scalable Parallel Genetic Algorithms
Artificial Intelligence Review
Multivariable Feedback Control: Analysis and Design
Multivariable Feedback Control: Analysis and Design
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Optimization Using Distributed Genetic Algorithms
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Implementing the Genetic Algorithm on Transputer Based Parallel Processing Systems
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
An Empirical Comparison of Selection Methods in Evolutionary Algorithms
Selected Papers from AISB Workshop on Evolutionary Computing
Grids and grid technologies for wide-area distributed computing
Software—Practice & Experience
The Anatomy of the Grid: Enabling Scalable Virtual Organizations
International Journal of High Performance Computing Applications
Covariance Matrix Adaptation for Multi-objective Optimization
Evolutionary Computation
Computational steering of a multi-objective evolutionary algorithm for engineering design
Engineering Applications of Artificial Intelligence
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
Toward a theory of evolution strategies: Self-adaptation
Evolutionary Computation
On the scalability of parallel genetic algorithms
Evolutionary Computation
Development of Web services-based Multidisciplinary Design Optimization framework
Advances in Engineering Software
Multi-objective evolution of robot neuro-controllers
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Distributed computing of Pareto-optimal solutions with evolutionary algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Multiobjective tuning of robust PID controllers using evolutionary algorithms
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Information Sciences: an International Journal
Many-Objective optimization: an engineering design perspective
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Expert Systems with Applications: An International Journal
A note on representations and variation operators
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
'Identifying the structure of nonlinear dynamic systems using multiobjective genetic programming
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Coupling conventional controller design methods, model based controller synthesis and simulation, and multi-objective evolutionary optimisation methods frequently results in an extremely computationally expensive design process. However, the emerging paradigm of grid computing provides a powerful platform for the solution of such problems by providing transparent access to large-scale distributed high-performance compute resources. As well as substantially speeding up the time taken to find a single controller design satisfying a set of performance requirements this grid-enabled design process allows a designer to effectively explore the solution space of potential candidate solutions. An example of this is in the multi-objective evolutionary design of robust controllers, where each candidate controller design has to be synthesised and the resulting performance of the compensated system evaluated by computer simulation. This paper introduces a grid-enabled framework for the multi-objective optimisation of computationally expensive problems which will then be demonstrated using and example of the multi-objective evolutionary design of a robust lateral stability controller for a real-world aircraft using H"~ loop shaping.