eps_MOEA

% Run an optimization using the epsilon-moea algorithm. Assumes that the % optimization problem is in canonical form - all target functions are to be % minimized. % % Arguments:  % creature - a description of the problem as returned from construct creature. % pop_size - number of individuals to use for the simulation. The more you use, % The better is the pareto front, but it"s slower and has diminishing  % returns. % conv_gens - after this many generations with no change in the archive  % population, the iteration stops. % nun_gens - maximum total number of generations, with or without convergence. % objectives - a function that given a population array returns the fitness  % array. % grid - the size of the hypercubes in the epsilon-dominance tests. % % Returns: % population - the population matrix after the latest iteration. % fitness - the