Genetic Programming (GP) is stochastic algorithm, there has been early attempts to explain its convergence with the Schmea Theorem (Holland 1975) for Genetic Algorithm adapted for GP such as (Koza ...
Genetic Algorithms seem like multiple simulated annealing instances, augmented with a crossover genetic operator. The crossover operator selects predefined genes from two different parent solutions to ...
I've been thinking: computing systems such as the Lambda Calculus and its variations are usually very simple and can be implemented in as few as ~80 lines of Haskell code. There is a self-interpreter ...
I'm looking for a formal definition of the space that consists of the dimensions gene 1-N and the fitness. In literature there is often the search space mentioned, but it only contains all genes. If ...
I have a few questions about linear genetic programming. I'm struggling to find much information on them, hopefully someone here can help me, it would be much appreciated. 1) Initialisation: When ...
What limits the current performance of genetic algorithms and neural networks? The principles underlying these techniques, at least the popular science presentation of these principles, suggests that ...
I was working on parallel implementation of Genetic Algorithm with MapReduce. I have found that in many papers authors are referencing OneMAX as problem they used to test scalability and convergence ...
Is it theoretically possible to reconstruct the contents of a file from its id using evolutionary computing? A file in this case can be a text, image, video or audio file. The 'id' in this case, ...
A colleague who works on genetic programming asked me the following question. I first tried to solve it based on a greedy approach, but on a second thought, I found a counterexample to the greedy ...