Cricket Team Selection Using Evolutionary Multi-objective Optimization
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- Ahmed F., Jindal A., Deb K. (2011) Cricket Team Selection Using Evolutionary Multi-objective Optimization. In: Panigrahi B.K., Suganthan P.N., Das S., Satapathy S.C. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2011. Lecture Notes in Computer Science, vol 7077. Springer, Berlin, Heidelberg
Selection of players for a high performance cricket team within a finite budget is a complex task which can be viewed as a constrained multi-objective optimization problem. In cricket team formation, batting strength and bowling strength of a team are the major factors affecting its performance and an optimum trade-off needs to be reached in formation of a good team. We propose a multi-objective approach using NSGA-II algorithm to optimize overall batting and bowling strength of a team and find team members in it. Using the information from trade-off front, a decision making approach is also proposed for final selection of team. Case study using a set of players auctioned in Indian Premier League, 4th edition has been taken and player’s current T-20 statistical data is used as performance parameter. This technique can be used by franchise owners and league managers to form a good team within budget constraints given by the organizers. The methodology is generic and can be easily extended to other sports like soccer, baseball etc.
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