Examining Mutation Landscapes in Grammar Based Genetic Programming
نویسندگان
چکیده
Representation is a very important component of any evolutionary algorithm. Changing the representation can cause an algorithm to perform very differently. Such a change can have an effect that is difficult to understand. This paper examines what happens to the grammatical evolution algorithm when replacing the commonly used context-free grammar representation with a tree-adjunct grammar representation. We model the landscapes produced when using integer flip mutation with both representations and compare these landscapes using visualisation methods little used in the field of genetic programming.
منابع مشابه
An investigation of the mutation operator using different representations in Grammatical Evolution
Grammatical evolution (GE) is a form of grammar-based genetic programming. A particular feature of GE is that it adopts a distinction between the genotype and phenotype similar to that which exists in nature by using a grammar to map between the genotype and phenotype. This study seeks to extend our understanding of GE by examining the impact of different genotypic representations in order to d...
متن کاملA Theoretical and Empirical Analysis of -ary Landscapes for Genetic Algorithms a Theoretical and Empirical Analysis of -ary Landscapes for Genetic Algorithms
Genetic algorithms (GAs) are probabilistic search algorithms that are loosely based on biological evolution. Analyzing genetic algorithms has proven di cult, for a variety of reasons, but a landscape paradigm that rigorously models the search of GAs has become increasingly popular in their analysis. So far much of this analysis concerns binary representations, where each member of the populatio...
متن کاملA Novel Experimental Analysis of the Minimum Cost Flow Problem
In the GA approach the parameters that influence its performance include population size, crossover rate and mutation rate. Genetic algorithms are suitable for traversing large search spaces since they can do this relatively fast and because the mutation operator diverts the method away from local optima, which will tend to become more common as the search space increases in size. GA’s are base...
متن کاملGrammar Model-based Program Evolution
In Evolutionary Computation, genetic operators, such as, mutation and crossover, are employed to variate the individuals to generate next population. However, these fixed, problem independent genetic operators may destroy the subsolution, usually called building blocks, instead of discovering and preserving them. One way to overcome this problem is to build a model based on the good individuals...
متن کاملDimensionality Reduction and Improving the Performance of Automatic Modulation Classification using Genetic Programming (RESEARCH NOTE)
This paper shows how we can make advantage of using genetic programming in selection of suitable features for automatic modulation recognition. Automatic modulation recognition is one of the essential components of modern receivers. In this regard, selection of suitable features may significantly affect the performance of the process. Simulations were conducted with 5db and 10db SNRs. Test and ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2011