Models for iterative global optimization
Models for iterative global optimization
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
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IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Locust swarms: a new multi-optima search technique
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
An Analysis of Locust Swarms on Large Scale Global Optimization Problems
ACAL '09 Proceedings of the 4th Australian Conference on Artificial Life: Borrowing from Biology
Optimization in Fractal and Fractured Landscapes Using Locust Swarms
ACAL '09 Proceedings of the 4th Australian Conference on Artificial Life: Borrowing from Biology
Selection strategies for initial positions and initial velocities in multi-optima particle swarms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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An inherent assumption in many search techniques is that information from existing solution(s) can help guide the search process to find better solutions. For example, memetic algorithms can use information from existing local optima to effectively explore a globally convex search space, and genetic algorithms assemble new solution candidates from existing solution components. At the extreme, the quality of a random solution may even be used to identify promising areas of the search space to explore. The best of several random solutions can be viewed as a "smart" start point for a greedy search technique, and the benefits of "smart" start points are demonstrated on several benchmark and real-world optimization problems. Although limitations exist, "smart" start points are most likely to be useful on continuous domain problems that have expensive solution evaluations.