I 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&#x27;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.<p>My stab at explaining GA&#x27;s: <a href="http:&#x2F;&#x2F;ai-maker.atrilla.net&#x2F;the-%EF%BB%BFgenetic-algorithms&#x2F;" rel="nofollow">http:&#x2F;&#x2F;ai-maker.atrilla.net&#x2F;the-%EF%BB%BFgenetic-algorithms&#x2F;</a>