What will be the difference between Reinforcement learning and Genetic Algorithm? The stackoverflow question here has mixed responses http://stackoverflow.com/questions/12411197/can-evolutionary.... According to me I have implemented Reinforcement learning once http://somedeepthoughtsblog.tumblr.com/post/134793589864/mat..., they look same as both boils down to exploring and exploiting the environment for maximum reward.
How Do Genetic Algorithms Work? [video]
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Re: How Do Genetic Algorithms Work? [video]
#12My issue with GA is that they are definitely the sexiest/best known evolutionary algorithm in their class, so they get used for a lot of things that they're not really designed for. GA work best if you have lots of discrete parameters that combine in weird ways to determine fitness (you know, like DNA). I'm a structural engineer, and most search problems I run into have a smaller number of continuously valued paramet…
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 metaheuristic…
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'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/
Re: How Do Genetic Algorithms Work? [video]
#13What will be the difference between Reinforcement learning and Genetic Algorithm? The stackoverflow question here has mixed responses http://stackoverflow.com/questions/12411197/can-evolutionary... . According to me I have implemented Reinforcement learning once http://somedeepthoughtsblog.tumblr.com/post/134793589864/mat... , they look same as both boils down to exploring and exploiting the environment for maximum r…
Reinforcement learning refers to the general problem of reward maximization where the agent must try solutions for itself and get feedback of the results via some kind of evaluation / fitness function. (As contrasted with supervised learning, where it the agent would be given examples, and would try to learn from that). Genetic algorithms are one specific class of optimization algorithms which accomplish this.
Exploration and / vs exploitation is just a natural consequence of trying to make best sense of the unknown environment. Different algorithms have different strategies for this, and are generally suited for different classes of problems (generally, the issue would be matching the nature of the problem with the nature of the optimization algorithm - which brings up things like the no free lunch theorem).
Re: How Do Genetic Algorithms Work? [video]
#14On a side note, the item id of this post is a palindrome: "11066011", which you can often see in GA algorithms because of how sequence splits happen.