Genetic Algorithm – solving a matrix with hard and soft constraints
I’m writing a Genetic Program that I need some advice on for crossover operations. The GP is attempting to find the best solution for a matrix that has hard row constraints and softer column constraints.
MATLAB: Best fitness vs mean fitness, initial range
Based on the example of Rastrigin’s function. At the plot function, if I chose ‘best fitness’, on the same graph ‘mean fitness’ will also be plotted. I understand well about ‘best fitness’ whereby it plots the best function value in each generation versus iteration number. It will reach value zero after some times.
What is the fitness landscape for minimal + viable solutions?
Let’s say I’m trying to find a number from 1 .. 100. All numbers in this range are “valid”, in that they could be interpreted as potential solutions.
Let’s say the ideal number is 50. And all numbers >= 50 are “feasible” in that they actually solve the problem. And all numbers < 50 are “not feasible” (but still valid).
How would you code a scenario like this with a fitness function (assuming that the landscape is similar to but more complex than this contrived example)? Do you give “bonuses” to valid solutions? Do you measure how far an unfeasible solution has left to go before becoming optimal? And do you penalize excessive solutions?
using ant colony algorithm to create cross word game [closed]
Closed 11 years ago.
Is this really necessary for solving this problem?
I came across the following article which presents the problem:
Is this really necessary for solving this problem?
I came across the following article which presents the problem:
Is this really necessary for solving this problem?
I came across the following article which presents the problem:
Is this really necessary for solving this problem?
I came across the following article which presents the problem:
New nodes joining distributed genetic algorithm
I’m sort of torn on what to do for implementation of my distributed genetic algorithm problem. I would like to be able to have nodes join and part at will and not take down the whole system. But this introduces the problem of mismatch of generations. Often times a genetic algorithm simulation is capped by a certain amount of generations and if I have a node that is on 75 out of 100 generations and a brand new node joins the cluster I’m not sure if I should fake it and start at 75 and copy one of the other nodes as a starting point or have it start out at 0 and potentially have the results delayed until the end of execution of the new node. I was hoping someone had some input on what they could see as problems in addition to a long wait time with this new node if I start at 0, I am struggling to think of what could go wrong in both approaches other than that.
New nodes joining distributed genetic algorithm
I’m sort of torn on what to do for implementation of my distributed genetic algorithm problem. I would like to be able to have nodes join and part at will and not take down the whole system. But this introduces the problem of mismatch of generations. Often times a genetic algorithm simulation is capped by a certain amount of generations and if I have a node that is on 75 out of 100 generations and a brand new node joins the cluster I’m not sure if I should fake it and start at 75 and copy one of the other nodes as a starting point or have it start out at 0 and potentially have the results delayed until the end of execution of the new node. I was hoping someone had some input on what they could see as problems in addition to a long wait time with this new node if I start at 0, I am struggling to think of what could go wrong in both approaches other than that.