Two self-adaptive crossover operators for genetic programming (1996)

by P J Angeline
Venue:Advances in Genetic Programming 2
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Parameter control in evolutionary algorithms

by A. E. Eiben, Z. Michalewicz, M. Schoenauer, J. E. Smith - IEEE Transactions on Evolutionary Computation
"... Summary. The issue of setting the values of various parameters of an evolutionary algorithm is crucial for good performance. In this paper we discuss how to do this, beginning with the issue of whether these values are best set in advance or are best changed during evolution. We provide a classifica ..."
Abstract - Cited by 365 (42 self) - Add to MetaCart
Summary. The issue of setting the values of various parameters of an evolutionary algorithm is crucial for good performance. In this paper we discuss how to do this, beginning with the issue of whether these values are best set in advance or are best changed during evolution. We provide a classification of different approaches based on a number of complementary features, and pay special attention to setting parameters on-the-fly. This has the potential of adjusting the algorithm to the problem while solving the problem. This paper is intended to present a survey rather than a set of prescriptive details for implementing an EA for a particular type of problem. For this reason we have chosen to interleave a number of examples throughout the text. Thus we hope to both clarify the points we wish to raise as we present them, and also to give the reader a feel for some of the many possibilities available for controlling different parameters. 1
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Adaptive and self-adaptive evolutionary computation,” in Computational Intelligence: A Dynamic System Perspective

by Peter J. Angeline , 1995
"... This paper reviews the various studies that have introduced adaptive and self-adaptive parameters into Evolutionary Computations. A formal definition of an adaptive evolutionary computation is provided with an analysis of the types of adaptive and self-adaptive parameter update rules currently in us ..."
Abstract - Cited by 99 (3 self) - Add to MetaCart
This paper reviews the various studies that have introduced adaptive and self-adaptive parameters into Evolutionary Computations. A formal definition of an adaptive evolutionary computation is provided with an analysis of the types of adaptive and self-adaptive parameter update rules currently in use. Previous stud-ies are reviewed and placed into a categorization that helps to illustrate their simi-larities and differences.
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Genetic Programming and Autoconstructive Evolution with the Push Programming Language

by Lee Spector, Alan Robinson - Genetic Programming and Evolvable Machines , 2002
"... Push is aprogxAI""1 langxA desigxA for the expression ofevolving proging within an evolutionary computation system. This article describes Push and illustrates some of the opportunities that it presents for evolutionary computation. Two evolutionary computation systems, PushGP and Push ..."
Abstract - Cited by 68 (18 self) - Add to MetaCart
Push is aprogxAI""1 langxA desigxA for the expression ofevolving proging within an evolutionary computation system. This article describes Push and illustrates some of the opportunities that it presents for evolutionary computation. Two evolutionary computation systems, PushGP and Pushpop, are described in detail. PushGP is ag-[fi88 prog8AI"y1 system that evolves Pushprog""x to solve computational problems. Pushpop, an "autoconstructive evolution" system, also evolves Push prog"[fi but does so while simultaneouslyevolving its own evolutionary mechanisms.

Genetic Programming and Data Structures

by William Benjamin Langdon , 1996
"... This thesis investigates the evolution and use of abstract data types within Genetic Programming (GP). In genetic programming the principles of natural evolution (fitness based selection and recombination) acts on program code to automatically generate computer programs. The research in this thesis ..."
Abstract - Cited by 59 (28 self) - Add to MetaCart
This thesis investigates the evolution and use of abstract data types within Genetic Programming (GP). In genetic programming the principles of natural evolution (fitness based selection and recombination) acts on program code to automatically generate computer programs. The research in this thesis is motivated by the observation from software engineering that data abstraction (e.g. via abstract data types) is essential in programs created by human programmers. We investigate whether abstract data types can be similarly beneficial to the automatic production of programs using GP. GP can automatically "evolve" programs which solve non-trivial problems but few experiments have been reported where the evolved programs explicitly manipulate memory and yet memory is an essential component of most computer programs. So far work on evolving programs that explicitly use memory has principally used either problem specific memory models or a simple indexed memory model consisting of a single glo...

A Revised Comparison of Crossover and Mutation in Genetic Programming

by Sean Luke, Lee Spector - Genetic Programming 1997: Proceedings of the Second Annual Conference , 1998
"... In [Luke and Spector 1997] we presented a comprehensive suite of data comparing GP crossover and point mutation over four domains and a wide range of parameter settings. Unfortunately, the results were marred by statistical flaws. This revision of the study eliminates these flaws, with three times a ..."
Abstract - Cited by 52 (2 self) - Add to MetaCart
In [Luke and Spector 1997] we presented a comprehensive suite of data comparing GP crossover and point mutation over four domains and a wide range of parameter settings. Unfortunately, the results were marred by statistical flaws. This revision of the study eliminates these flaws, with three times as much the data as the original experiments had. Our results again show that crossover does have some advantageover mutation given the right parameter settings (primarily larger population sizes), though the difference between the two surprisingly small. Further, the results are complex, suggesting that the big picture is more complicated than is commonly believed. 1 Introduction The genetic algorithms and evolutionary programming fields have long been at odds over the proper chief operator for generating new populations from previous ones. Genetic algorithms proponents favor crossover, while evolutionary programming's philosophy emphasizes mutation. Most justification for using crossover ...

Co-evolving Intertwined Spirals

by Hugues Juille, Jordan B. Pollack - in Proceedings of the Fifth Annual Conference on Evolutionary Programming , 1996
"... We recently solved the two spirals problem, a difficult neural network benchmark classification problem, using the genetic programming primitives set up by [Koza, 1992]. Instead of using absolute fitness, we use a relative fitness based on a competition for coverage of the data set. This is a form o ..."
Abstract - Cited by 49 (15 self) - Add to MetaCart
We recently solved the two spirals problem, a difficult neural network benchmark classification problem, using the genetic programming primitives set up by [Koza, 1992]. Instead of using absolute fitness, we use a relative fitness based on a competition for coverage of the data set. This is a form of co-evolutionary search because the fitness function changes with the population. Because niches are opened by proportionate reproduction, rather than crowded out, and because of the crossover operator, we find solutions which have a nice modular structure. Our experiments used our Massively Parallel Genetic Programming (MPGP) system running on a SIMD machine of 4096 processors, the Maspar MP-2.

Massively Parallel Genetic Programming

by Hugues Juille, Jordan B. Pollack , 1996
"... Introduction The idea of simulating a MIMD machine using a SIMD architecture is not new ([8, 15]). One of the original ideas for the Connection Machine ([8]) was that it could simulate other parallel architectures. Indeed, in the extreme, each processor on a SIMD architecture can simulate a univers ..."
Abstract - Cited by 33 (5 self) - Add to MetaCart
Introduction The idea of simulating a MIMD machine using a SIMD architecture is not new ([8, 15]). One of the original ideas for the Connection Machine ([8]) was that it could simulate other parallel architectures. Indeed, in the extreme, each processor on a SIMD architecture can simulate a universal Turing machine (TM). With different turing machine specifications stored in each local memory, each processor would simply have its own tape, tape head, state table and state pointer, and the simulation would be performed by repeating the basic TM operations simultaneously. Of course, such a simulation would be very inefficient, and difficult to program, but would have the advantage of being really MIMD, where no SIMD processor would be in idle state, until its simulated machine halts. Now let us consider an alternative idea, that each SIMD processor would simulate an individual stored program computer using a simple instruction set. For each step of the simulation, the SIMD syste

Evolving Evolutionary Algorithms Using Linear Genetic Programming

by Mihai Oltean - Evolutionary Computation , 2005
"... A new model for evolving Evolutionary Algorithms is proposed in this paper. The model is based on the Linear Genetic Programming (LGP) technique. Every LGP chromosome encodes an EA which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization, the Trav ..."
Abstract - Cited by 32 (7 self) - Add to MetaCart
A new model for evolving Evolutionary Algorithms is proposed in this paper. The model is based on the Linear Genetic Programming (LGP) technique. Every LGP chromosome encodes an EA which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization, the Traveling Salesman Problem and the Quadratic Assignment Problem are evolved by using the considered model. Numerical experiments show that the evolved Evolutionary Algorithms perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.
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Dynamics of co-evolutionary learning

by Hugues Juille, Jordan B. Pollack - In Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior , 1996
"... Co-evolutionary learning, which involves the embedding of adaptive learning agents in a t-ness environment which dynamically responds to their progress, is a potential solution for many technological chicken and egg problems, and is at the heart of several recent and surprising successes, such as Si ..."
Abstract - Cited by 31 (8 self) - Add to MetaCart
Co-evolutionary learning, which involves the embedding of adaptive learning agents in a t-ness environment which dynamically responds to their progress, is a potential solution for many technological chicken and egg problems, and is at the heart of several recent and surprising successes, such as Sim's arti cial robot and Tesauro's backgammon player. We recently solved the two spirals problem, a di cult neural network benchmark classi cation problem, using the genetic programming primitives set up by [Koza, 1992]. Instead of using absolute tness, we use a relative tness [Angeline & Pollack, 1993] based on a competition for coverage of the data set. As the population reproduces, the tness function driving the selection changes, and subproblem niches are opened, rather than crowded out. The solutions found by our method have a symbiotic structure which suggests that by holding niches open, crossover is better able to discover modular building blocks. 1

Autoconstructive Evolution: Push, PushGP, and Pushpop

by Lee Spector - PROCEEDINGS OF THE GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, GECCO-2001, 137–146 , 2001
"... This paper is a preliminary report on autoconstructive evolution, a framework for evolutionary computation in which the machinery of reproduction and diversification (and thereby the machinery of evolution) evolves within the individuals of an evolving population of problem solvers. Autoconstr ..."
Abstract - Cited by 29 (12 self) - Add to MetaCart
This paper is a preliminary report on autoconstructive evolution, a framework for evolutionary computation in which the machinery of reproduction and diversification (and thereby the machinery of evolution) evolves within the individuals of an evolving population of problem solvers. Autoconstructive evolution is illustrated with Pushpop, an evolving population of programs expressed in the Push programming language. The Push programming language can also be used in a more traditional genetic programming framework and may have unique benefits when so employed; the PushGP system, which uses traditional genetic programming techniques to evolve Push programs, is also described.