CODEWITHSUNDEEP
c++matlabmathematical-optimizationevolutionary-algorithm
user667804
user667804

Reputation: 760

Evolutionary Algorithm without an objective function

I'm currently trying to find good parameters for my program (about 16 parameters and execution of the program takes about a minute). Evolutionary algorithms seemed like a nice idea and I wanted to see how they perform.

Unfortunately I don't have a good fitness function because the variance of my objective function is very high (I can not run it often enough without waiting until 2016). I can, however, compute which set of parameters is better (test two configurations against each other). Do you know if there are evolutionary algorithms that only use that information? Are there other optimization techniques more suitable? For this project I'm using C++ and MATLAB.

// Update: Thank you very much for the answers. Both look promising but I will need a few days to evaluate them. Sorry for the delay.

Upvotes: 3

Views: 408

Answers (2)

Yanshuai Cao
Yanshuai Cao

Reputation: 1297

If your pairwise test gives a proper total ordering, i.e. if a >= b, and b >= c implies a >= c, and some other conditions . Then maybe you can construct a ranking objective on the fly, and use CMA-ES to optimize it. CMA-ES is an evolutionary algorithm and is invariant to order preserving transformation of function value, and angle-preserving transformation of inputs. Furthermore because it's a second order method, its convergence is very fast comparing to other derivative-free search heuristics, especially in higher dimensional problems where random search like genetic algorithms take forever.

Upvotes: 3

John Coleman
John Coleman

Reputation: 51998

If you can compare solutions in a pairwise fashion then some sort of tournament selection approach might be good. The Wikipedia article describes using it for a genetic algorithm but it is easily applied to an evolutionary algorithm. What you do is repeatedly select a small set of solutions from the population and have a tournament among them. For simplicity the tournament size could be a power of 2. If it was 8 then pair those 8 up at random and compare them, selecting 4 winners. Pair those up and select 2 winners. In a final round -- select an overall tournament winner. This solution can then be mutated 1 or more times to provide member(s) for the next generation.

Upvotes: 2

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