The Current State and Future of Search Based Software Engineering
FOSE '07 2007 Future of Software Engineering
Brief paper: A swarm intelligence approach to the synthesis of two-dimensional IIR filters
Engineering Applications of Artificial Intelligence
Evolutionary synthesis of low-sensitivity equalizers using adjacency matrix representation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Search based software engineering
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
Search-based software engineering: Trends, techniques and applications
ACM Computing Surveys (CSUR)
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Evolutionary approaches have been used in a large variety of design domains, from aircraft engineering to the designs of analog filters. Many of these approaches use measures to improve the variety of solutions in the population. One such measure is clustering. In this paper, clustering and Pareto optimisation are combined into a single evolutionary design algorithm. The population is split into a number of clusters, and parent and offspring selection, as well as fitness calculation, are performed on a per-cluster basis. The objective of this is to prevent the system from converging prematurely to a local minimum and to encourage a number of different designs that fulfil the design criteria. Our approach is demonstrated in the domain of digital filter design. Using a polar coordinate based pole-zero representation, two different lowpass filter design problems are explored. The results are compared to designs created by a human expert. They demonstrate that the evolutionary process is able to create designs that are competitive with those created using a conventional design process by a human expert. They also demonstrate that each evolutionary run can produce a number of different designs with similar fitness values, but very different characteristics.