Impact of commutative and non-commutative functions on symbolic regression with ACGP

CZ Janikow, J Aleshunas - 2013 IEEE Congress on …, 2013‏ - ieeexplore.ieee.org
Genetic Programming, as other evolutionary methods, uses selection to drive its search
toward better solutions, but its search operators are uninformed and perform uniform search …

Cost-benefit analysis of using heuristics in acgp

J Aleshunas, C Janikow - 2011 IEEE Congress of Evolutionary …, 2011‏ - ieeexplore.ieee.org
Constrained Genetic Programming (CGP) is a method of searching the Genetic
Programming search space non-uniformly, giving preferences to certain subspaces …

[PDF][PDF] Cost-benefit Analysis of Using Heuristics in ACGP

C Janikow - constraints, 2011‏ - cs.umsl.edu
Constrained Genetic Programming (CGP) is a method of searching the Genetic
Programming search space non-uniformly, giving preferences to certain subspaces …

[کتاب][B] GP Representation Space Reduction Using a Tiered Search Scheme

JJ Aleshunas - 2013‏ - search.proquest.com
The size and complexity of a GP representation space is defined by the set of functions and
terminals used, the arity of those functions, and the maximal depth of candidate solution …

[کتاب][B] Enhancing Scalability in Genetic Programming with Adaptable Constraints, Type Constraints and Automatically Defined Functions

G Gerules - 2019‏ - search.proquest.com
Genetic Programming is a type of biological inspired machine learning. It is composed of a
population of stochastic individuals. Those individuals can exchange portions of themselves …

[PDF][PDF] Building Block Emergence in Genetic Programming

J Aleshunas‏ - mercury.webster.edu
Background Genetic Programming (GP) is an evolutionary method that is adept at solving
optimal instruction set problems. Some examples of problems that GP is well suited for are …