scholarly journals Evolving continuous optimisers from scratch

2021 ◽  
Author(s):  
Michael A. Lones
Keyword(s):  
Genetic Programming ◽  
Search Space ◽  
General Purpose ◽  
Problem Types ◽  
Broad Variety ◽  
Turing Complete

AbstractThis work uses genetic programming to explore the space of continuous optimisers, with the goal of discovering novel ways of doing optimisation. In order to keep the search space broad, the optimisers are evolved from scratch using Push, a Turing-complete, general-purpose, language. The resulting optimisers are found to be diverse, and explore their optimisation landscapes using a variety of interesting, and sometimes unusual, strategies. Significantly, when applied to problems that were not seen during training, many of the evolved optimisers generalise well, and often outperform existing optimisers. This supports the idea that novel and effective forms of optimisation can be discovered in an automated manner. This paper also shows that pools of evolved optimisers can be hybridised to further increase their generality, leading to optimisers that perform robustly over a broad variety of problem types and sizes.

scholarly journals Feature Selection using Genetic Programming

2019 ◽  
Vol 3 (2) ◽  
pp. 11-18
Author(s):  
George Mweshi
Keyword(s):  
Feature Selection ◽  
Genetic Programming ◽  
Information Gain ◽  
Principal Component ◽  
Search Space ◽  
Classification Performance ◽  
The Other ◽  
Searching Strategy ◽  
Mining Algorithms ◽  
Feature Selection Techniques

Extracting useful and novel information from the large amount of collected data has become a necessity for corporations wishing to maintain a competitive advantage. One of the biggest issues in handling these significantly large datasets is the curse of dimensionality. As the dimension of the data increases, the performance of the data mining algorithms employed to mine the data deteriorates. This deterioration is mainly caused by the large search space created as a result of having irrelevant, noisy and redundant features in the data. Feature selection is one of the various techniques that can be used to remove these unnecessary features. Feature selection consequently reduces the dimension of the data as well as the search space which in turn increases the efficiency and the accuracy of the mining algorithms. In this paper, we investigate the ability of Genetic Programming (GP), an evolutionary algorithm searching strategy capable of automatically finding solutions in complex and large search spaces, to perform feature selection. We implement a basic GP algorithm and perform feature selection on 5 benchmark classification datasets from UCI repository. To test the competitiveness and feasibility of the GP approach, we examine the classification performance of four classifiers namely J48, Naives Bayes, PART, and Random Forests using the GP selected features, all the original features and the features selected by the other commonly used feature selection techniques i.e. principal component analysis, information gain, relief-f and cfs. The experimental results show that not only does GP select a smaller set of features from the original features, classifiers using GP selected features achieve a better classification performance than using all the original features. Furthermore, compared to the other well-known feature selection techniques, GP achieves very competitive results.

Efficient Evolution of Modular Robot Control via Genetic Programming

2013 ◽  
pp. 59-85
Author(s):  
Tüze Kuyucu ◽  
Ivan Tanev ◽  
Katsunori Shimohara
Keyword(s):  
Multiobjective Optimization ◽  
Genetic Programming ◽  
Evolutionary Computation ◽  
Fast Moving ◽  
Search Space ◽  
Multiple Objectives ◽  
Experimental Results ◽  
Robotic Systems ◽  
Linear Fashion ◽  
Fast Evolution

In Genetic Programming (GP), most often the search space grows in a greater than linear fashion as the number of tasks required to be accomplished increases. This is a cause for one of the greatest problems in Evolutionary Computation (EC): scalability. The aim of the work presented here is to facilitate the evolution of control systems for complex robotic systems. The authors use a combination of mechanisms specifically designed to facilitate the fast evolution of systems with multiple objectives. These mechanisms are: a genetic transposition inspired seeding, a strongly-typed crossover, and a multiobjective optimization. The authors demonstrate that, when used together, these mechanisms not only improve the performance of GP but also the reliability of the final designs. They investigate the effect of the aforementioned mechanisms on the efficiency of GP employed for the coevolution of locomotion gaits and sensing of a simulated snake-like robot (Snakebot). Experimental results show that the mechanisms set forth contribute to significant increase in the efficiency of the evolution of fast moving and sensing Snakebots as well as the robustness of the final designs.

Automatic Differentiation of the General-Purpose Computational Fluid Dynamics Package FLUENT

2006 ◽  
Vol 129 (5) ◽  
pp. 652-658 ◽  
Author(s):  
Christian H. Bischof ◽  
H. Martin Bücker ◽  
Arno Rasch ◽  
Emil Slusanschi ◽  
Bruno Lang
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Automatic Differentiation ◽  
General Purpose ◽  
Computational Techniques ◽  
Software Packages ◽  
Broad Variety ◽  
Industrial Software ◽  
Crucial Ingredient ◽  
Numerical Approaches

Derivatives are a crucial ingredient to a broad variety of computational techniques in science and engineering. While numerical approaches for evaluating derivatives suffer from truncation error, automatic differentiation is accurate up to machine precision. The term automatic differentiation comprises a set of techniques for mechanically transforming a given computer program to another one capable of evaluating derivatives. A common misconception about automatic differentiation is that this technique only works on local pieces of fairly simple code. Here, it is shown that automatic differentiation is not only applicable to small academic codes, but scales to advanced industrial software packages. In particular, the general-purpose computational fluid dynamics software package FLUENT is transformed by automatic differentiation.

scholarly journals Feature selection in evolving job shop dispatching rules with genetic programming

2021 ◽  
Author(s):  
Yi Mei ◽  
Mengjie Zhang ◽  
Su Nyugen
Keyword(s):  
Genetic Programming ◽  
Domain Knowledge ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Search Space ◽  
Dispatching Rules ◽  
Terminal Set ◽  
Selection Approach ◽  
Personal Use ◽  
Priority Function

Genetic Programming (GP) has been successfully used to automatically design dispatching rules in job shop scheduling. The goal of GP is to evolve a priority function that will be used to order the waiting jobs at each decision point, and decide the next job to be processed. To this end, the proper terminals (i.e. job shop features) have to be decided. When evolving the priority function, various job shop features can be included in the terminal set. However, not all the features are helpful, and some features are irrelevant to the rule. Including irrelevant features into the terminal set enlarges the search space, and makes it harder to achieve promising areas. Thus, it is important to identify the important features and remove the irrelevant ones to improve the GP-evolved rules. This paper proposes a domain-knowledge-free feature ranking and selection approach. As a result, the terminal set is significantly reduced and only the most important features are selected. The experimental results show that using only the selected features can lead to significantly better GP-evolved rules on both training and unseen test instances. © Mei 2016. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in 'GECCO '16: Proceedings of the Genetic and Evolutionary Computation Conference', https://doi.org/10.1145/2908812.2908822.

Genetic Programming Using a Turing-Complete Representation

2005 ◽  
pp. 61-81 ◽  
Author(s):  
Taro Yabuki ◽  
Hitoshi Iba
Keyword(s):  
Genetic Programming ◽  
Evolutionary Computing ◽  
Recurrent Network ◽  
Complete Representation ◽  
Representation Scheme ◽  
Turing Complete ◽  
General Method

In this chapter, a new representation scheme for Genetic Programming (GP) is proposed. We need a Turing-complete representation for a general method of generating programs automatically; that is, the representation must be able to express any algorithms. Our representation is a recurrent network consisting of trees (RTN), which is proved to be Turing-complete. In addition, it is applied to the tasks of generating language classifiers and a bit reverser. As a result, RTN is shown to be usable in evolutionary computing.

scholarly journals Using Genetic Programming with Prior Formula Knowledge to Solve Symbolic Regression Problem

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Qiang Lu ◽  
Jun Ren ◽  
Zhiguang Wang
Keyword(s):  
Experimental Data ◽  
Evolutionary Algorithms ◽  
Genetic Programming ◽  
Search Space ◽  
Research Field ◽  
Benchmark Problems ◽  
Regression Problem ◽  
Mathematical Expressions ◽  
The Right

A researcher can infer mathematical expressions of functions quickly by using his professional knowledge (called Prior Knowledge). But the results he finds may be biased and restricted to his research field due to limitation of his knowledge. In contrast, Genetic Programming method can discover fitted mathematical expressions from the huge search space through running evolutionary algorithms. And its results can be generalized to accommodate different fields of knowledge. However, sinceGPhas to search a huge space, its speed of finding the results is rather slow. Therefore, in this paper, a framework of connection between Prior Formula Knowledge andGP(PFK-GP) is proposed to reduce the space ofGPsearching. The PFK is built based on the Deep Belief Network (DBN) which can identify candidate formulas that are consistent with the features of experimental data. By using these candidate formulas as the seed of a randomly generated population,PFK-GPfinds the right formulas quickly by exploring the search space of data features. We have comparedPFK-GPwith ParetoGPon regression of eight benchmark problems. The experimental results confirm that thePFK-GPcan reduce the search space and obtain the significant improvement in the quality of SR.

scholarly journals A genetic programming hyper-heuristic for the multidimensional knapsack problem

Kybernetes ◽  
2014 ◽  
Vol 43 (9/10) ◽  
pp. 1500-1511 ◽  
Author(s):  
John H Drake ◽  
Matthew Hyde ◽  
Khaled Ibrahim ◽  
Ender Ozcan
Keyword(s):  
Genetic Programming ◽  
Knapsack Problem ◽  
Research Effort ◽  
Search Space ◽  
Future Research ◽  
Test Problems ◽  
Content Type ◽  
Multidimensional Knapsack ◽  
Constructive Heuristics ◽  
High Level

Purpose – Hyper-heuristics are a class of high-level search techniques which operate on a search space of heuristics rather than directly on a search space of solutions. The purpose of this paper is to investigate the suitability of using genetic programming as a hyper-heuristic methodology to generate constructive heuristics to solve the multidimensional 0-1 knapsack problem Design/methodology/approach – Early hyper-heuristics focused on selecting and applying a low-level heuristic at each stage of a search. Recent trends in hyper-heuristic research have led to a number of approaches being developed to automatically generate new heuristics from a set of heuristic components. A population of heuristics to rank knapsack items are trained on a subset of test problems and then applied to unseen instances. Findings – The results over a set of standard benchmarks show that genetic programming can be used to generate constructive heuristics which yield human-competitive results. Originality/value – In this work the authors show that genetic programming is suitable as a method to generate reusable constructive heuristics for the multidimensional 0-1 knapsack problem. This is classified as a hyper-heuristic approach as it operates on a search space of heuristics rather than a search space of solutions. To our knowledge, this is the first time in the literature a GP hyper-heuristic has been used to solve the multidimensional 0-1 knapsack problem. The results suggest that using GP to evolve ranking mechanisms merits further future research effort.

scholarly journals A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules

2020 ◽  
Author(s):  
Su Nguyen ◽  
Yi Mei ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Production Systems ◽  
Search Space ◽  
Automated Design ◽  
Dispatching Rules ◽  
Programming Algorithm ◽  
Massachusetts Institute Of Technology ◽  
Institute Of Technology

© 2018 Massachusetts Institute of Technology. Designing effective dispatching rules for production systems is a difficult and timeconsuming task if it is done manually. In the last decade, the growth of computing power, advanced machine learning, and optimisation techniques has made the automated design of dispatching rules possible and automatically discovered rules are competitive or outperform existing rules developed by researchers. Genetic programming is one of the most popular approaches to discovering dispatching rules in the literature, especially for complex production systems. However, the large heuristic search space may restrict genetic programming from finding near optimal dispatching rules. This article develops a new hybrid genetic programming algorithm for dynamic job shop scheduling based on a new representation, a new local search heuristic, and efficient fitness evaluators. Experiments show that the new method is effective regarding the quality of evolved rules. Moreover, evolved rules are also significantly smaller and contain more relevant attributes.

scholarly journals A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules

2020 ◽  
Author(s):  
Su Nguyen ◽  
Yi Mei ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Production Systems ◽  
Search Space ◽  
Automated Design ◽  
Dispatching Rules ◽  
Programming Algorithm ◽  
Massachusetts Institute Of Technology ◽  
Institute Of Technology

© 2018 Massachusetts Institute of Technology. Designing effective dispatching rules for production systems is a difficult and timeconsuming task if it is done manually. In the last decade, the growth of computing power, advanced machine learning, and optimisation techniques has made the automated design of dispatching rules possible and automatically discovered rules are competitive or outperform existing rules developed by researchers. Genetic programming is one of the most popular approaches to discovering dispatching rules in the literature, especially for complex production systems. However, the large heuristic search space may restrict genetic programming from finding near optimal dispatching rules. This article develops a new hybrid genetic programming algorithm for dynamic job shop scheduling based on a new representation, a new local search heuristic, and efficient fitness evaluators. Experiments show that the new method is effective regarding the quality of evolved rules. Moreover, evolved rules are also significantly smaller and contain more relevant attributes.

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