A Comparison of Several Linear Genetic Programming Techniques

2004

In this paper, we use multi-objective techniques to compare different genetic programming systems, permitting our comparison to concentrate on the effect of representation and separate out the effects of different search space sizes and search algorithms. Experimental results are given, comparing the performance and search behavior of Tree Adjoining Grammar Guided Genetic Programming (TAG3P) and Standard Genetic Programming (GP) on some standard problems.

KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 2019

Genetic Programming (GP) is an intelligence technique whereby computer programs are encoded as a set of genes which are evolved utilizing a Genetic Algorithm (GA). In other words, the GP employs novel optimization techniques to modify computer programs; imitating the way humans develop programs by progressively rewriting them for solving problems automatically. Trial programs are frequently altered in the search for obtaining superior solutions due to the base is GA. These are evolutionary search techniques inspired by biological evolution such as mutation, reproduction, natural selection, recombination, and survival of the fittest. The power of GAs is being represented by an advancing range of applications; vector processing, quantum computing, VLSI circuit layout, and so on. But one of the most significant uses of GAs is the automatic generation of programs. Technically, the GP solves problems automatically without having to tell the computer specifically how to process it. To meet this requirement, the GP utilizes GAs to a "population" of trial programs, traditionally encoded in memory as tree-structures. Trial programs are estimated using a "fitness function" and the suited solutions picked for re-evaluation and modification such that this sequence is replicated until a "correct" program is generated. GP has represented its power by modifying a simple program for categorizing news stories, executing optical character recognition, medical signal filters, and for target identification, etc. This paper reviews existing literature regarding the GPs and their applications in different scientific fields and aims to provide an easy understanding of various types of GPs for beginners.

Journal of Intelligent and Robotic Systems, 2006

Engineered Biomimicry, 2013

We discuss Evolutionary Computation, in particular Genetic Programming, as examples of drawing inspiration from biological systems. We set the choice of evolution as a source for inspiration in context, discuss the history of Evolutionary Computation and its variants before looking more closely at Genetic Programming. After a discussion of methods and the state-of-theart, we review application areas of Genetic Programming and its strength in providing human-competitive solutions.

Genetic and evolutionary computation, 2018

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Genetic and Evolutionary Computation Series, 2008

, except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights.

2007

The thesis is about linear genetic programming (LGP), a machine learning approac h that evolves computer programs as sequences of imperative instructions. Two fundament al differences to the more common tree-based variant (TGP) may be identified. These are the graph-based functional structure of linear genetic programs, on the one hand , and the existence of structurally noneffective code, on the other hand. The two major objectives of this work comprise (1) the development of more advan ced methods and variation operators to produce better and more compact program solut ions and (2) the analysis of general EA/GP phenomena in linear GP, including intron c ode, neutral variations, and code growth, among others. First, we introduce efficient algorithms for extracting features of the imperati ve and functional structure of linear genetic programs. In doing so, especially the detecti on and elimination of noneffective code during runtime will turn out as a powerful tool to accelerate the time-consuming step of fitness evaluation in GP. Variation operators are discussed systematically for the linear program represen tation. We will demonstrate that so called effective instruction mutations achieve the b est performance in terms of solution quality. These mutations operate only on the (stru cturally) effective code and restrict the mutation step size to one instruction. One possibility to further improve their performance is to explicitly increase t he probability of neutral variations. As a second, more time-efficient alternative we explicitl y control the mutation step size on the effective code (effective step size). Minimum steps do not allow more than one effective instruction to change its effectiveness status. That is, only a single node may be connected to or disconnected from the effective graph compone nt. It is an interesting phenomenon that, to some extent, the effective code becomes mo re robust against destructions over the generations already implicitly. A special concern of this thesis is to convince the reader that there are some s erious arguments for using a linear representation. In a crossover-based comparison LGP has been found superior to TGP over a set of benchmark problems. Furthermore, linear solutions turned out to be more compact than tree solutions due to (1) multiple usage of subgraph results and (2) implicit parsimony pressure by structurally noneffectiv e code. The phenomenon of code growth is analyzed for different linear genetic operators . When applying instruction mutations exclusively almost only neutral variations may be held responsible for the emergence and propagation of intron code. It is noteworthy t hat linear genetic programs may not grow if all neutral variation effects are reject ed and if the variation step size is minimum. For the same reasons effective instruction m utations realize an implicit complexity control in linear GP which reduces a possible neg ative effect of code growth to a minimum. Another noteworthy result in this context is that p rogram size is strongly increased by crossover while it is hardly influenced by mutatio n even if step sizes are not explicitly restricted. ii Finally, we investigate program teams as one possibility to increase the dimensi on of genetic programs. It will be demonstrated that much more powerful solutions may be found by teams than by individuals. Moreover, the complexity of team solutions r emains surprisingly small compared to individual programs. Both is the result of specia lization and cooperation of team members.

2009

• Papers developing techniques tested on small-scale problems include discussion of how to apply those techniques to real-world problems, while papers tackling real-world problems have employed techniques developed from theoretical work to gain insights.

Soft Computing, 2007

This paper proposes a new grammar-guided genetic programming (GGGP) system by introducing two original genetic operators: crossover and mutation, which most influence the evolution process. The first, the so-called grammar-based crossover operator, strikes a good balance between search space exploration and exploitation capabilities and, therefore, enhances GGGP system performance. And the second is a grammar-based mutation operator, based on the crossover, which has been designed to generate individuals that match the syntactical constraints of the context-free grammar that defines the programs to be handled. The use of these operators together in the same GGGP system assures a higher convergence speed and less likelihood of getting trapped in local optima than other related approaches. These features are shown throughout the comparison of the results achieved by the proposed system with other important crossover and mutation methods in two experiments: a laboratory problem and the real-world task of breast cancer prognosis.

Computing Research Repository, 2001

Gene expression programming (GEP) is, like genetic algorithms (GAs) and genetic programming (GP), a genetic algorithm as it uses populations of individuals, selects them according to fitness, and introduces genetic variation using one or more genetic operators . The fundamental difference between the three algorithms reside in the nature of the individuals: in GAs the individuals are linear strings of fixed length (chromosomes); in GP the individuals are non-linear entities of different sizes and shapes (parse trees); and in GEP the individuals are encoded as linear strings of fixed length (the genome or chromosomes) which are afterwards expressed as non-linear entities of different sizes and shapes (simple diagram representations or expression trees).