An empirical study on the accuracy of computational effort in Genetic Programming

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. D. F. Barrero, M. D. R-Moreno, B. Castaño, and D. Camacho, "An empirical study on the accuracy of computational effort in Genetic Programming", in IEEE Congress on Evolutionary Computation (CEC), 2011, pp. 1164 - 1171Some commonly used performance measures in Genetic Programming are those defined by John Koza in his first book. These measures, mainly computational effort and number of individuals to be processed, estimate the performance of the algorithm as well as the difficulty of a problem. Although Koza's performance measures have been widely used in the literature, their behaviour is not well known. In this paper we study the accuracy of these measures and advance in the understanding of the factors that influence them. In order to achieve this goal, we report an empirical study that attempts to systematically measure the effects of two variability sources in the estimation of the number of individuals to be processed and the computational effort. The results obtained in those experiments suggests that these measures, in common experimental setups, and under certain circumstances, might have a high relative error.This work was partially supported by the MICYT project ABANT (TIN2010-19872) and Castilla-La Mancha project PEII09- 0266-664

Unifying a Geometric Framework of Evolutionary Algorithms and Elementary Landscapes Theory

Evolutionary algorithms (EAs) are randomised general-purpose strategies, inspired by natural evolution, often used for finding (near) optimal solutions to problems in combinatorial optimisation. Over the last 50 years, many theoretical approaches in evolutionary computation have been developed to analyse the performance of EAs, design EAs or measure problem difficulty via fitness landscape analysis. An open challenge is to formally explain why a general class of EAs perform better, or worse, than others on a class of combinatorial problems across representations. However, the lack of a general unified theory of EAs and fitness landscapes, across problems and representations, makes it harder to characterise pairs of general classes of EAs and combinatorial problems where good performance can be guaranteed provably. This thesis explores a unification between a geometric framework of EAs and elementary landscapes theory, not tied to a specific representation nor problem, with complementary strengths in the analysis of population-based EAs and combinatorial landscapes. This unification organises around three essential aspects: search space structure induced by crossovers, search behaviour of population-based EAs and structure of fitness landscapes. First, this thesis builds a crossover classification to systematically compare crossovers in the geometric framework and elementary landscapes theory, revealing a shared general subclass of crossovers: geometric recombination P-structures, which covers well-known crossovers. The crossover classification is then extended to a general framework for axiomatically analysing the population behaviour induced by crossover classes on associated EAs. This shows the shared general class of all EAs using geometric recombination P-structures, but no mutation, always do the same abstract form of convex evolutionary search. Finally, this thesis characterises a class of globally convex combinatorial landscapes shared by the geometric framework and elementary landscapes theory: abstract convex elementary landscapes. It is formally explained why geometric recombination P-structure EAs expectedly can outperform random search on abstract convex elementary landscapes related to low-order graph Laplacian eigenvalues. Altogether, this thesis paves a way towards a general unified theory of EAs and combinatorial fitness landscapes

Artificial Intelligence Techniques Based Modeling of Bicycle Level of Service for Urban Road Segments

Bicycle is one of the essential modes of transportation in a developing country like India. There is no safety to bicyclists in the mixed traffic and high speed moving vehicles. Hence we should emphasize on them by considering factors affecting bicyclists comfort in planning and design stage itself. In India there is heterogeneous traffic where we find interactions between bicycles and vehicles. Since no methodologies are available there is a need to develop a methodology that gives the perceive comfort level of bicyclists on road segments in mid-sized cities. In this study BLOS Model is developed using three techniques namely Artificial Neural Networks (ANN) and Multi Gene Genetic Programming Methods (MGGP) and Multi linear Regression (MLR). Overall 59 segment data is used for analysis which is collected from Rourkela, Bhubaneswar and Rajahmundry. Eight significant input parameters are considered in the models namely, width of outside through lane (WOTL), peak hour volume for a single lane (PHV/L), pavement condition index (PCI), land use pattern (LU), average traffic speed (S), interruption by public transits (IBPT), on-street parking activities (P) and presence of commercial driveways (D).BLOS model equations are developed for all three techniques. Sensitivity Analysis is carried out to determine the important parameters highly affecting the BLOS. Performances of these models have been tested in terms of several statistical parameters such as: correlation coefficient (R), maximum absolute error (MAE), absolute average error (AAE) and root mean square error (RMSE) and the best model is obtained. In present study MGGP based BLOS model has good performance compared to ANN and MLR techniques

Genetic programming for predicting protein networks

Proceeding of: 11th Ibero-American Conference on AI (IBERAMIA 2008), Lisbon, Portugal, 14-17 Octubre 2008One of the definitely unsolved main problems in molecular biology is the protein-protein functional association prediction problem. Genetic Programming (GP) is applied to this domain. GP evolves an expression, equivalent to a binary classifier, which predicts if a given pair of proteins interacts. We take advantages of GP flexibility, particularly, the possibility of defining new operations. In this paper, the missing values problem benefits from the definition of if-unknown, a new operation which is more appropriate to the domain data semantics. Besides, in order to improve the solution size and the computational time, we use the Tarpeian method which controls the bloat effect of GP. According to the obtained results, we have verified the feasibility of using GP in this domain, and the enhancement in the search efficiency and interpretability of solutions due to the Tarpeian method.Publicad

Evolving robots: from simple behaviours to complete systems

Building robots is generally considered difficult, because the designer not only has to predict the interaction between the robot and the environment, but also has to deal with the ensuing problems. This thesis examines the use of the evolutionary approach in designing robots; the explorations range from evolving simple behaviours for real robots, to complex behaviours (also for real robots), and finally to complete robot systems — including controllers and body plans. A framework is presented for evolving robot control systems. It includes two components: a task independent Genetic Programming sub-system and a task dependent controller evaluation sub-system. The performance evaluation of each robot controller is done in a simulator to reduce the evaluation time, and then the evolved controllers are downloaded to a real robot for performance verification. In addition, a special rep¬ resentation is designed for the reactive robot controller. It is succinct and can capture the important characteristics of a reactive control system, so that the evolutionary system can efficiently evolve the controllers of the desired behaviours for the robots. The framework has been successfully used to evolve controllers for real robots to achieve a variety of simple tasks, such as obstacle avoidance, safe exploration and box-pushing. A methodology is then proposed to scale up the system to evolve controllers for more complicated tasks. It involves adopting the architecture of a behaviour-based system, and evolving separate behaviour controllers and arbitrators for coordination. This allows robot controllers for more complex skills to be constructed in an incremental manner. Therefore the whole control system becomes easy to evolve; moreover, the resulting control system can be explicitly distributed, understandable to the system designer, and easy to maintain. The methodology has been used to evolve control systems for more complex tasks with good results. Finally, the evolutionary mechanism of the framework described above is extended to include a Genetic Algorithm sub-system for the co-evolution of robot body plans — structuralparametersofphysicalrobotsencodedaslinearstringsofrealnumbers. An individual in the extended system thus consists of a brain(controller) and a body. Whenever the individual is evaluated, the controller is executed on the corresponding body for a period of time to measure the performance. In such a system the Genetic Programming part evolves the controller; and the Genetic Algorithm part, the robot body. The results show that the complete robot system can be evolved in this manner. i

GPR: A Data Mining Tool Using Genetic Programming

This paper proposes an inductive data mining technique (named GPR) based on genetic programming. Unlike other mining systems, the particularity of our technique is its ability to discover business rules that satisfy multiple (and possibly conflicting) decision or search criteria simultaneously. We present a step-by-step method to implement GPR, and introduce a prototype that generates production rules from real life data. We also report in this article on the use of GPR in an organization that seeks to understand how its employees make decisions in a voluntary separation program. Using a personnel database of 12,787 employees with 35 descriptive variables, our technique is able to discover employees\u27 hidden decision making patterns in the form of production rules. As our approach does not require any domain specific knowledge, it can be used without any major modification in different domains

An Investigation of Supervised Learning in Genetic Programming

Centre for Intelligent Systems and their Applicationsstudentship 9314680This thesis is an investigation into Supervised Learning (SL) in Genetic Programming (GP). With its flexible tree-structured representation, GP is a type of Genetic Algorithm, using the Darwinian idea of natural selection and genetic recombination, evolving populations of solutions over many generations to solve problems. SL is a common approach in Machine Learning where the problem is presented as a set of examples. A good or fit solution is one which can successfully deal with all of the examples.In common with most Machine Learning approaches, GP has been used to solve many trivial problems. When applied to larger and more complex problems, however, several difficulties become apparent. When focusing on the basic features of GP, this thesis highlights the immense size of the GP search space, and describes an approach to measure this space. A stupendously flexible but frustratingly useless representation, Anarchically Automatically Defined Functions, is described. Some difficulties associated with the normal use of the GP operator Crossover (perhaps the most common method of combining GP trees to produce new trees) are demonstrated in the simple MAX problem. Crossover can lead to irreversible sub-optimal GP performance when used in combination with a restriction on tree size. There is a brief study of tournament selection which is a common method of selecting fit individuals from a GP population to act as parents in the construction of the next generation.The main contributions of this thesis however are two approaches for avoiding the fitness evaluation bottleneck resulting from the use of SL in GP. to establish the capability of a GP individual using SL, it must be tested or evaluated against each example in the set of training examples

Evolutionary program induction directed by logic grammars.

by Wong Man Leung.Thesis (Ph.D.)--Chinese University of Hong Kong, 1995.Includes bibliographical references (leaves 227-236).List of Figures --- p.iiiList of Tables --- p.viChapter Chapter 1 : --- Introduction --- p.1Chapter 1.1. --- Automatic programming and program induction --- p.1Chapter 1.2. --- Motivation --- p.6Chapter 1.3. --- Contributions of the research --- p.8Chapter 1.4. --- Outline of the thesis --- p.11Chapter Chapter 2 : --- An Overview of Evolutionary Algorithms --- p.13Chapter 2.1. --- Evolutionary algorithms --- p.13Chapter 2.2. --- Genetic Algorithms (GAs) --- p.15Chapter 2.2.1. --- The canonical genetic algorithm --- p.16Chapter 2.2.1.1. --- Selection methods --- p.21Chapter 2.2.1.2. --- Recombination methods --- p.24Chapter 2.2.1.3. --- Inversion and Reordering --- p.27Chapter 2.2.2. --- Implicit parallelism and the building block hypothesis --- p.28Chapter 2.2.3. --- Steady state genetic algorithms --- p.32Chapter 2.2.4. --- Hybrid algorithms --- p.33Chapter 2.3. --- Genetic Programming (GP) --- p.34Chapter 2.3.1. --- Introduction to the traditional GP --- p.34Chapter 2.3.2. --- Automatic Defined Function (ADF) --- p.41Chapter 2.3.3. --- Module Acquisition (MA) --- p.44Chapter 2.3.4. --- Strongly Typed Genetic Programming (STGP) --- p.49Chapter 2.4. --- Evolution Strategies (ES) --- p.50Chapter 2.5. --- Evolutionary Programming (EP) --- p.55Chapter Chapter 3 : --- Inductive Logic Programming --- p.59Chapter 3.1. --- Inductive concept learning --- p.59Chapter 3.2. --- Inductive Logic Programming (ILP) --- p.62Chapter 3.2.1. --- Interactive ILP --- p.64Chapter 3.2.2. --- Empirical ILP --- p.65Chapter 3.3. --- Techniques and methods of ILP --- p.67Chapter Chapter 4 : --- Genetic Logic Programming and Applications --- p.74Chapter 4.1. --- Introduction --- p.74Chapter 4.2. --- Representations of logic programs --- p.76Chapter 4.3. --- Crossover of logic programs --- p.81Chapter 4.4. --- Genetic Logic Programming System (GLPS) --- p.87Chapter 4.5. --- Applications --- p.90Chapter 4.5.1. --- The Winston's arch problem --- p.91Chapter 4.5.2. --- The modified Quinlan's network reachability problem --- p.92Chapter 4.5.3. --- The factorial problem --- p.95Chapter Chapter 5 : --- The logic grammars based genetic programming system (LOGENPRO) --- p.100Chapter 5.1. --- Logic grammars --- p.101Chapter 5.2. --- Representations of programs --- p.103Chapter 5.3. --- Crossover of programs --- p.111Chapter 5.4. --- Mutation of programs --- p.126Chapter 5.5. --- The evolution process of LOGENPRO --- p.130Chapter 5.6. --- Discussion --- p.132Chapter Chapter 6 : --- Applications of LOGENPRO --- p.134Chapter 6.1. --- Learning functional programs --- p.134Chapter 6.1.1. --- Learning S-expressions using LOGENPRO --- p.134Chapter 6.1.2. --- The DOT PRODUCT problem --- p.137Chapter 6.1.2. --- Learning sub-functions using explicit knowledge --- p.143Chapter 6.2. --- Learning logic programs --- p.148Chapter 6.2.1. --- Learning logic programs using LOGENPRO --- p.148Chapter 6.2.2. --- The Winston's arch problem --- p.151Chapter 6.2.3. --- The modified Quinlan's network reachability problem --- p.153Chapter 6.2.4. --- The factorial problem --- p.154Chapter 6.2.5. --- Discussion --- p.155Chapter 6.3. --- Learning programs in C --- p.155Chapter Chapter 7 : --- Knowledge Discovery in Databases --- p.159Chapter 7.1. --- Inducing decision trees using LOGENPRO --- p.160Chapter 7.1.1. --- Decision trees --- p.160Chapter 7.1.2. --- Representing decision trees as S-expressions --- p.164Chapter 7.1.3. --- The credit screening problem --- p.166Chapter 7.1.4. --- The experiment --- p.168Chapter 7.2. --- Learning logic program from imperfect data --- p.174Chapter 7.2.1. --- The chess endgame problem --- p.177Chapter 7.2.2. --- The setup of experiments --- p.178Chapter 7.2.3. --- Comparison of LOGENPRO with FOIL --- p.180Chapter 7.2.4. --- Comparison of LOGENPRO with BEAM-FOIL --- p.182Chapter 7.2.5. --- Comparison of LOGENPRO with mFOILl --- p.183Chapter 7.2.6. --- Comparison of LOGENPRO with mFOIL2 --- p.184Chapter 7.2.7. --- Comparison of LOGENPRO with mFOIL3 --- p.185Chapter 7.2.8. --- Comparison of LOGENPRO with mFOIL4 --- p.186Chapter 7.2.9. --- Comparison of LOGENPRO with mFOIL5 --- p.187Chapter 7.2.10. --- Discussion --- p.188Chapter 7.3. --- Learning programs in Fuzzy Prolog --- p.189Chapter Chapter 8 : --- An Adaptive Inductive Logic Programming System --- p.192Chapter 8.1. --- Adaptive Inductive Logic Programming --- p.192Chapter 8.2. --- A generic top-down ILP algorithm --- p.196Chapter 8.3. --- Inducing procedural search biases --- p.200Chapter 8.3.1. --- The evolution process --- p.201Chapter 8.3.2. --- The experimentation setup --- p.202Chapter 8.3.3. --- Fitness calculation --- p.203Chapter 8.4. --- Experimentation and evaluations --- p.204Chapter 8.4.1. --- The member predicate --- p.205Chapter 8.4.2. --- The member predicate in a noisy environment --- p.205Chapter 8.4.3. --- The multiply predicate --- p.206Chapter 8.4.4. --- The uncle predicate --- p.207Chapter 8.5. --- Discussion --- p.208Chapter Chapter 9 : --- Conclusion and Future Work --- p.210Chapter 9.1. --- Conclusion --- p.210Chapter 9.2. --- Future work --- p.217Chapter 9.2.1. --- Applying LOGENPRO to discover knowledge from databases --- p.217Chapter 9.2.2. --- Learning recursive programs --- p.218Chapter 9.2.3. --- Applying LOGENPRO in engineering design --- p.220Chapter 9.2.4. --- Exploiting parallelism of evolutionary algorithms --- p.222Reference --- p.227Appendix A --- p.23