genetic-algorithm

I am a newbie to Genetic Algorithm. I am trying to predict the pattern of occurrences of rules. For example, I have a set of rules defined as below. Rule 1, Rule 2, Rule 3, Rule 4, Rule 5, Rule 6, For a given date, I could have only Rule 2, Rule 3 and Rule 6 are used. So I would represent this data as a string as stated below 0 1 1 0 0 1 where 1 denotes that the rule is used and 0 denotes that the rule never get used on that day. So I would have set of data for 5 days as below 011001, 100010, 110011, 101010, 111100, What I would like to achieve here is to predict the the 6th day data. I have

How do you find an optimum learning rule for a given problem, say a multiple category classification? I was thinking of using Genetic Algorithms, but I know there are issues surrounding performance. I am looking for real world examples where you have not used the textbook learning rules, and how you found those learning rules. Nice question BTW . classification algorithms can be classified using many Characteristics like: What does the algorithm strongly prefer (or what type of data that is most suitable for this algorithm). training overhead. (does it take a lot of time to be trained) When is

I have a problem which I am trying to solve with genetic algorithms. The problem is selecting some subset (say 4) of 100 integers (these integers are just ids that represent something else). Order does not matter, the solution to the problem is a SET of integers not an ordered list. I have a good fitness function but am having trouble with the crossover function. I want to be able to mate the following two chromosomes: [1 2 3 4] and [3 4 5 6] into something useful. Clearly I cannot use the typical crossover function because I could end up with duplicates in my children which would represent

I have implemented a Genetic Algorithm to solve the Traveling Salesman Problem (TSP). When I use only mutation, I find better solutions than when I add in crossover. I know that normal crossover methods do not work for TSP, so I implemented both the Ordered Crossover and the PMX Crossover methods, and both suffer from bad results. Here are the other parameters I'm using: Mutation : Single Swap Mutation or Inverted Subsequence Mutation ( as described by Tiendil here ) with mutation rates tested between 1% and 25%. Selection : Roulette Wheel Selection Fitness function : 1 / distance of tour

I try to learn and implement a simple genetic algorithm library for my project. At this time, evolution, selection of population is ready, and I'm trying to implement a simple good mutation operator like the Gaussian mutation operator (GMO) for my genetic evolution engine in Java and Scala. I find some information on Gaussian mutation operator (GMO) into the paper A mutation operator based on a Pareto ranking for multi-objective evolutionary algorithms (P.M. Mateo, I. Alberto), page 6 and 7. But I have some problem to find other information on how to implement this Gaussian mutation operator

I'm working on a feed forward artificial neural network (ffann) that will take input in form of a simple calculation and return the result (acting as a pocket calculator). The outcome wont be exact. The artificial network is trained using genetic algorithm on the weights. Currently my program gets stuck at a local maximum at: 5-6% correct answers, with 1% error margin 30 % correct answers, with 10% error margin 40 % correct answers, with 20% error margin 45 % correct answers, with 30% error margin 60 % correct answers, with 40% error margin I currently use two different genetic algorithms: The

I'm looking for material on using genetic algorithms to evolve algorithmic behaviour/decision strategies, rather than parametric values. I can't find much. Could someone please point me in the right direction? Links would be awesome, but I guess I'd be okay with search keywords, technichal terminology I don't know about and should be looking for, etc. As Gordon Murray Dent said, when evolutionary programming is used to create functions, it's generally referred to as "genetic programming" while "genetic algorithms" typically refers (somewhat oddly) to function parameter optimization. The