Cooling schedules for optimal annealing
Mathematics of Operations Research
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Best-so-far vs. where-you-are: implications for optimal finite-time annealing
Systems & Control Letters
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
When a genetic algorithm outperforms hill-climbing
Theoretical Computer Science
Large Barrier Trees for Studying Search
IEEE Transactions on Evolutionary Computation
Benefits of a population: five mechanisms that advantage population-based algorithms
IEEE Transactions on Evolutionary Computation
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The performance, on a given problem, of search heuristics such as simulated annealing and descent with variable mutation can be described as a function of, and optimised over, the parameters of the heuristic (e.g. the annealing or mutation schedule). We describe heuristics as Markov processes; the search for optimal parameters is then rendered feasible by the use of level-accessible barrier trees for state amalgamation. Results are presented for schedules minimising ''where-you-are'' and ''best-so-far'' cost, over binary perceptron, spin-glass and Max-SAT problems. We also compute first-passage time for several ''toy heuristics'', including constant-temperature annealing and fixed-rate mutation search.