Adapting temperature for some randomized local search algorithms
A heuristic learning algorithm is introduced to adapt the temperature for a randomized local search procedure based on a Boltzmann machine. Experimental results are given to confirm that the method improves both efficiency and more especially reliability. Further experiments are described which investigate how the choice of temperature affects the algorithm’s performance. Key-words: randomized local search procedure, graph colouring, adapting temperature, combinatorial