Metaheuristic algorithms, such as genetic algorithms and simulated annealing, are search techniques that are inspired by nature. They aim to avoid a problem encountered by traditional search techniques such as hill climbing - the danger of getting stuck at a local optimum. Many achieve this by adding a stochastic element, such as the ability to accept a move from a candidate solution to one that appears worse.

Metaheuristic algorithms have been applied to a wide range of optimisation or search problems. However, it is only relatively recently that they have been used in software engineering. The motivation behind this work is simple - many software engineering problems can be reformulated as search problems. We want solutions to our problems and one approach to this is to apply techniques that allow us to search for a solution.

• A.S. Kalaji, R. M. Hierons, and S. Swift: A Testability Transformation Approach for State-Based Programs, 1st International Symposium on Search-Based Software Engineering (SSBSE 2009), 2009.
• A.S. Kalaji, R. M. Hierons, and S. Swift: Generating Feasible Transition Paths for Testing from an Extended Finite State Machine (EFSM), 2nd IEEE International Conference on Software Testing, Verification and Validation (ICST 2009), 2009.
• Z. Li, M. Harman, and R. M. Hierons: Search Algorithms for Regression Test Case Prioritization, IEEE Transactions on Software Engineering, 33 4 , pp. 225-237, 2007.
• K. Derderian, R. M. Hierons, M. Harman, and Q. Guo: Automated Unique Input Output sequence generation for conformance testing, The Computer Journal, 49 3, pp. 331-344, 2006.
• Q. Guo, R. M. Hierons, M. Harman, and K. Derderian: Constructing Multiple Unique Input/Output Sequences Using Metaheuristic Optimisation Techniques, IEE Proceedings – Software, 152 3, pp.127-140, 2005.
• D. Fatiregun, M. Harman and R. M. Hierons: Evolving Transformation Sequences using Genetic Algorithms, 4th IEEE Workshop on Source Code Analysis and Manipulation (SCAM 2004), September 14th-15th, 2004, Chicago, Illinois, USA, pp. 65-74, 2004.
• K. Adamopoulos, M. Harman, R. M. Hierons: Mutation Testing Using Genetic Algorithms: A Co-evolution Approach, AAAI Genetic and Evolutionary Computation Conference 2004 (GECCO 2004), pp. 1338-1349, 2004.
• K. Derderian, R. M. Hierons, M. Harman, and Q. Guo: Input sequence generation for testing of communicating finite state machines (CFSMs) using genetic algorithms, AAAI Genetic and Evolutionary Computation Conference 2004 (GECCO 2004), pp. 1429-1430, 2004.
• J. Clark, J. J. Dolado, M. Harman, R. M. Hierons, B. Jones, M. Lumkin, B. Mitchell, S. Mancoridis, K. Rees, M. Roper and M. Shepperd: Reformulating Software Engineering as a Search Problem, IEE Proceedings – Software, 150 3, pp. 161-175, 2003.
• Q. Guo, R.M. Hierons, M. Harman and K. Derderian: Computing Unique Input/Output Sequences Using Genetic Algorithms, 3rd Formal Approaches to Testing (FATES’03), 6th October 2003), published in LNCS volume 2931, pp. 164-177, 2003.
• K. Mahdavi, M. Harman and R.M. Hierons: A Multiple Hill Climbing Approach to Software Module Clustering, 19th IEEE International Conference on Software Maintenance (ICSM 2003), Amsterdam, The Netherlands, pp. 315-324, 2003.
• M. Harman, L. Hu, R. M. Hierons, C. Fox, S. Danicic, Andre Baresel, Harmen Sthamer, and Joachim Wegener: Evolutionary Testing Supported by Slicing and Transformation, IEEE International Conference on Software Maintenance (ICSM 2002) Montreal, Canada, pp. 285, 2002.
• M. Harman, R.M. Hierons and M. Proctor: A New Representation and Crossover Operation for Search-Based Optimization of Software Modularization, AAAI Genetic and Evolutionary Computation Conference 2002 (GECCO 2002) . New York, USA, pp. 1351-1358, 2002.

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Last updated: 30 April 2009.