AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Simulation-based planning for planetary rover experiments
WSC '05 Proceedings of the 37th conference on Winter simulation
Tackling car sequencing problems using a generic genetic algorithm
Evolutionary Computation
Leap before you look: an effective strategy in an oversubscribed scheduling problem
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
An effective algorithm for project scheduling with arbitrary temporal constraints
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
A comparison of techniques for scheduling earth observing satellites
IAAI'04 Proceedings of the 16th conference on Innovative applications of artifical intelligence
A study of greedy, local search, and ant colony optimization approaches for car sequencing problems
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Mixed discrete and continuous algorithms for scheduling airborne astronomy observations
CPAIOR'05 Proceedings of the Second international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Crossover operators for the car sequencing problem
EvoCOP'07 Proceedings of the 7th European conference on Evolutionary computation in combinatorial optimization
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The AI optimization algorithm called "Squeaky-Wheel Optimization" (SWO) has proven very effective in a variety of real-world applications. Although the ideas behind SWO are more closely tied to those of local search such as hill-climbing, in some ways SWO can be thought of as an evolutionary algorithm. From that point of view SWO makes a number of design decisions that are at odds with the conventional wisdom of evolutionary algorithms, but not for any clear reasons. This suggests the possibility of improving on SWO by incorporating aspects of Genetic Algorithms that are known to be effective. We compare several algorithm variants on a set of constrained optimization benchmarks, and present some preliminary results suggesting that combining ideas from SWO with a more standard GA approach yields some significant improvements over both.