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Yes, I believe there was a paper that hillclimbing with random restarts almost always did better than genetic algorithms. However does it really matter? It mig
by Houshalter 11y ago
Yes, I believe there was a paper that hillclimbing with random restarts almost always did better than genetic algorithms.
However does it really matter? It might save a little bit of time at best. But almost everyone has heard of genetic algorithms, so it's easier to talk about, than talking about particle swarm optimizations or whatever. Also genetic algorithms tend to excite people in a way that other metaheuristics just don't. People are fascinated with genetic algorithms, not so much with hillclimbing.
- ai_maker 11y agoI would say that hill climbing with random restarts works better because you must first define a cost function in a precise (and concise) symbolic mathematical way. Very much similar to simulated annealing, stochastic gradient descent and the like. However, GA's can be applied regardless of the piece of knowledge that is required to build this cost function. GA is a universal optimisation technique: as long as you can encode features in a gene form (this is very convenient for binary attributes) and rate the fitting of that gene you are ready to search the optimum. My stab at explaining GA's: http://ai-maker.atrilla.net/the-%EF%BB%BFgenetic-algorithms/ http://ai-maker.atrilla.net/the-%EF%BB%BFgenetic-algorithms/