Multi-threaded memory efficient crossover in C++ for generational genetic programming
نویسندگان
چکیده
منابع مشابه
External Memory Search for Verification of Multi-threaded C++ Programs
With the advent of multi-core processors, the need for development of multi-threaded softwares has become indispensable. Verification of multi-threaded programs, particularly those that involve sharing of memory resources, poses a greater challenge than their sequential counterparts. A certain class of software model checking problems can be transformed to AI search problems in graphs. Search a...
متن کاملMulti-class Classification using BFS Crossover in Genetic Programming
Multi-class classification is a kind of classification task which involves processing an input object and then assigning this object to one of the more than two possible classes.Crossover operation is considered to be a primary genetic operator for modifying the program structures in Genetic Programming (GP). Genetic Programming is a random process, and it does not guarantee results. Randomness...
متن کاملHomologous Crossover in Genetic Programming
In recent years, the genetic programming crossover operator has been criticized on both theoretical and empirical grounds. This paper introduces a new crossover operator for linear genomes that encourages the emergence of positional homology in the population. Preliminary experimental results suggest that this approach is a promising direction for redesign of the mechanism of crossover.
متن کاملCrossover Bias in Genetic Programming
Path length, or search complexity, is an understudied property of trees in genetic programming. Unlike size and depth measures, path length directly measures the balancedness or skewedness of a tree. Here a close relative to path length, called visitation length, is studied. It is shown that a population undergoing standard crossover will introduce a crossover bias in the visitation length. Thi...
متن کاملAsynchronous Multi-Threaded Model for Genetic Algorithms
In this paper we present a parallel implementation of genetic algorithms using a shared memory model designed to take advantage of multi-core processor platforms. Our algorithm divides the problems into subproblems as opposed to the usual approach of dividing the population into niches. We propose an approach where the threads do not have to synchronize their evolution at any level and compare ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: ACM SIGEVOlution
سال: 2020
ISSN: 1931-8499
DOI: 10.1145/3430913.3430914