Initialization method for grammar-guided genetic programming

This paper proposes a new tree-generation algorithm for grammar-guided genetic programming that includes a parameter to control the maximum size of the trees to be generated. An important feature of this algorithm is that the initial populations generated are adequately distributed in terms of tree size and distribution within the search space. Consequently, genetic programming systems starting from the initial populations generated by the proposed method have a higher convergence speed. Two different problems have been chosen to carry out the experiments: a laboratory test involving searching for arithmetical equalities and the real-world task of breast cancer prognosis. In both problems, comparisons have been made to another five important initialization methods.     

遗传算法与进化规划的比较研究

遗传算法和退化规划是目前工程应用研究中最普遍的两种进化算法，由于它们的来源及原理的不同导致它们在生物基础、算法操作及实施细节上均存在很大差异，适最终影响到它们的实施效果及性能。通过系统的理论分析及函数仿真实验研究表明。进化规划无论是生物基础、算法实施选是计算性能方面都明显优于遗传算法，是处理工程优化问题的一种更理想的方法。     

Scaling Up Inductive Logic Programming: An Evolutionary Wrapper Approach

Inductive logic programming (ILP) algorithms are classification algorithms that construct classifiers represented as logic programs. ILP algorithms have a number of attractive features, notably the ability to make use of declarative background (user-supplied) knowledge. However, ILP algorithms deal poorly with large data sets (>10⁴ examples) and their widespread use of the greedy set-covering algorithm renders them susceptible to local maxima in the space of logic programs.This paper presents a novel approach to address these problems based on combining the local search properties of an inductive logic programming algorithm with the global search properties of an evolutionary algorithm. The proposed algorithm may be viewed as an evolutionary wrapper around a population of ILP algorithms.The evolutionary wrapper approach is evaluated on two domains. The chess-endgame (KRK) problem is an artificial domain that is a widely used benchmark in inductive logic programming, and Part-of-Speech Tagging is a real-world problem from the field of Natural Language Processing. In the latter domain, data originates from excerpts of the Wall Street Journal. Results indicate that significant improvements in predictive accuracy can be achieved over a conventional ILP approach when data is plentiful and noisy.     

Comparison study on nature-inspired optimization algorithms for optimization back analysis of underground engineering

Optimization back analysis is the most common approach to displacement back analysis for underground engineering. However, this is a non-convex problem that requires the use of nature-inspired global optimization algorithms. Therefore, the present study will investigate on the suitability of six state-of-the-art nature-inspired algorithms for elastic back analysis and elastic–plastic back analysis. These algorithms include improved genetic algorithm, immunized evolutionary programming, particle swarm optimization, continuous ant colony optimization, artificial bee colony and black hole algorithm. Numerical results indicate that immunized evolutionary programming is overall the best algorithm followed by the black hole algorithm; while, the improved genetic algorithm is the worst optimizer. Meanwhile, using elastic back analysis, the sensitivity analysis of the main input parameters for these nature-inspired optimization algorithms has been conducted. At last, the comparative results have been verified by using in one real underground roadway in Huainan coal mine of China.

     

Grammar-based generation of stochastic local search heuristics through automatic algorithm configuration tools

Several grammar-based genetic programming algorithms have been proposed in the literature to automatically generate heuristics for hard optimization problems. These approaches specify the algorithmic building blocks and the way in which they can be combined in a grammar; the best heuristic for the problem being tackled is found by an evolutionary algorithm that searches in the algorithm design space defined by the grammar.In this work, we propose a novel representation of the grammar by a sequence of categorical, integer, and real-valued parameters. We then use a tool for automatic algorithm configuration to search for the best algorithm for the problem at hand. Our experimental evaluation on the one-dimensional bin packing problem and the permutation flowshop problem with weighted tardiness objective shows that the proposed approach produces better algorithms than grammatical evolution, a well-established variant of grammar-based genetic programming. The reasons behind such improvement lie both in the representation proposed and in the method used to search the algorithm design space.     

Designing robust volunteer-based evolutionary algorithms

This paper tackles the design of scalable and fault-tolerant evolutionary algorithms computed on volunteer platforms. These platforms aggregate computational resources from contributors all around the world. Given that resources may join the system only for a limited period of time, the challenge of a volunteer-based evolutionary algorithm is to take advantage of a large amount of computational power that in turn is volatile. The paper analyzes first the speed of convergence of massively parallel evolutionary algorithms. Then, it provides some guidance about how to design efficient policies to overcome the algorithmic loss of quality when the system undergoes high rates of transient failures, i.e. computers fail only for a limited period of time and then become available again. In order to provide empirical evidence, experiments were conducted for two well-known problems which require large population sizes to be solved, the first based on a genetic algorithm and the second on genetic programming. Results show that, in general, evolutionary algorithms undergo a graceful degradation under the stress of losing computing nodes. Additionally, new available nodes can also contribute to improving the search process. Despite losing up to 90 % of the initial computing resources, volunteer-based evolutionary algorithms can find the same solutions in a failure-prone as in a failure-free run.     

Evolutionary programming techniques for economic load dispatch

Evolutionary programming has emerged as a useful optimization tool for handling nonlinear programming problems. Various modifications to the basic method have been proposed with a view to enhance speed and robustness and these have been applied successfully on some benchmark mathematical problems. But few applications have been reported on real-world problems such as economic load dispatch (ELD). The performance of evolutionary programs on ELD problems is examined and presented in this paper in two parts. In Part I, modifications to the basic technique are proposed, where adaptation is based on scaled cost. In Part II, evolutionary programs are developed with adaptation based on an empirical learning rate. Absolute, as well as relative, performance of the algorithms are investigated on ELD problems of different size and complexity having nonconvex cost curves where conventional gradient-based methods are inapplicable.     

An overview of evolutionary algorithms: practical issues and common pitfalls

An overview of evolutionary algorithms is presented covering genetic algorithms, evolution strategies, genetic programming and evolutionary programming. The schema theorem is reviewed and critiqued. Gray codes, bit representations and real-valued representations are discussed for parameter optimization problems. Parallel Island models are also reviewed, and the evaluation of evolutionary algorithms is discussed.     

Evolutionary consequences of coevolving targets

Most evolutionary optimization models incorporate a fitness evaluation that is based on a predefined static set of test cases or problems. In the natural evolutionary process, selection is of course not based on a static fitness evaluation. Organisms do not have to combat every existing disease during their lifespan; organisms of one species may live in different or changing environments; different species coevolve. This leads to the question of how information is integrated over many generations. This study focuses on the effects of different fitness evaluation schemes on the types of genotypes and phenotypes that evolve. The evolutionary target is a simple numerical function. The genetic representation is in the form of a program (i.e., a functional representation, as in genetic programming). Many different programs can code for the same numerical function. In other words, there is a many-to-one mapping between "genotypes" (the programs) and "phenotypes". We compare fitness evaluation based on a large static set of problems and fitness evaluation based on small coevolving sets of problems. In the latter model very little information is presented to the evolving programs regarding the evolutionary target per evolutionary time step. In other words, the fitness evaluation is very sparse. Nevertheless the model produces correct solutions to the complete evolutionary target in about half of the simulations. The complete evaluation model, on the other hand, does not find correct solutions to the target in any of the simulations. More important, we find that sparse evaluated programs are better generalizable compared to the complete evaluated programs when they are evaluated on a much denser set of problems. In addition, the two evaluation schemes lead to programs that differ with respect to mutational stability; sparse evaluated programs are less stable than complete evaluated programs.     

Genetic programming for medical classification: a program simplification approach

This paper describes a genetic programming (GP) approach to medical data classification problems. In this approach, the evolved genetic programs are simplified online during the evolutionary process using algebraic simplification rules, algebraic equivalence and prime techniques. The new simplification GP approach is examined and compared to the standard GP approach on two medical data classification problems. The results suggest that the new simplification GP approach can not only be more efficient with slightly better classification performance than the basic GP system on these problems, but also significantly reduce the sizes of evolved programs. Comparison with other methods including decision trees, naive Bayes, nearest neighbour, nearest centroid, and neural networks suggests that the new GP approach achieved superior results to almost all of these methods on these problems. The evolved genetic programs are also easier to interpret than the “hidden patterns” discovered by the other methods.

A two-leveled symbiotic evolutionary algorithm for clustering problems

Because of its unsupervised nature, clustering is one of the most challenging problems, considered as a NP-hard grouping problem. Recently, several evolutionary algorithms (EAs) for clustering problems have been presented because of their efficiency for solving the NP-hard problems with high degree of complexity. Most previous EA-based algorithms, however, have dealt with the clustering problems given the number of clusters (K) in advance. Although some researchers have suggested the EA-based algorithms for unknown K clustering, they still have some drawbacks to search efficiently due to their huge search space. This paper proposes the two-leveled symbiotic evolutionary clustering algorithm (TSECA), which is a variant of coevolutionary algorithm for unknown K clustering problems. The clustering problems considered in this paper can be divided into two sub-problems: finding the number of clusters and grouping the data into these clusters. The two-leveled framework of TSECA and genetic elements suitable for each sub-problem are proposed. In addition, a neighborhood-based evolutionary strategy is employed to maintain the population diversity. The performance of the proposed algorithm is compared with some popular evolutionary algorithms using the real-life and simulated synthetic data sets. Experimental results show that TSECA produces more compact clusters as well as the accurate number of clusters.     

A survey of repair methods used as constraint handling techniques in evolutionary algorithms

This paper provides a survey of the most important repair heuristics used in evolutionary algorithms to solve constrained optimization problems. Popular techniques are reviewed, such as some crossover operators in permutation encoding, algorithms for fixing the number of 1s in binary encoded genetic algorithms, and more specialized techniques such as Hopfield neural networks, heuristics for graphs and trees, and repair heuristics in grouping genetic algorithms. The survey also gives some indications about the design and implementation of hybrid evolutionary algorithms, and provides a revision of the most important applications in which hybrid evolutionary techniques have been used.     

Functional search in economics using genetic programming

This paper discusses economic applications of a recently developed artificial intelligence technique-Koza's genetic programming (GP). GP is an evolutionary search method related to genetic algorithms. In GP, populations of potential solutions consist of executable computer algorithms, rather than coded strings. The paper provides an overview of how GP works, and illustrates with two applications: solving for the policy function in a simple optimal growth model, and estimating an unusual regression function. Results suggest that the GP search method can be an interesting and effective tool for economists.     

Evolving Evolutionary Algorithms with Patterns

A new model for evolving evolutionary algorithms (EAs) is proposed in this paper. The model is based on the multi expression programming (MEP) technique. Each MEP chromosome encodes an evolutionary pattern which is repeatedly used for generating the individuals of a new generation. The evolved pattern is embedded into a standard evolutionary scheme which is used for solving a particular problem. Several evolutionary algorithms for function optimization are evolved by using the considered model. The evolved evolutionary algorithms are compared with a human-designed genetic algorithm. Numerical experiments show that the evolved evolutionary algorithms can compete with standard approaches for several well-known benchmarking problems.     

Graph-based evolutionary design of arithmetic circuits

We present an efficient graph-based evolutionary optimization technique, called evolutionary graph generation (EGG), and the proposed approach is applied to the design of combinational and sequential arithmetic circuits based on parallel counter-tree architecture. The fundamental idea of EGG is to employ general circuit graphs as individuals and manipulate the circuit graphs directly using new evolutionary graph operations without encoding the graphs into other indirect representations, such as the bit strings used in genetic algorithm (GA) proposed by Holland (1992) and trees used in genetic programming (GP) proposed by Koza et al. (1997). In this paper, the EGG system is applied to the design of constant-coefficient multipliers and the design of bit-serial data-parallel adders. The results demonstrate the potential capability of EGG to solve the practical design problems for arithmetic circuits with limited knowledge of computer arithmetic algorithms. The proposed EGG system can help to simplify and speed up the process of designing arithmetic circuits and can produce better solutions to the given problem     

Automatic detection of trends in time-stamped sequences: an evolutionary approach

This paper presents an evolutionary algorithm for modeling the arrival dates in time-stamped data sequences such as newscasts, e-mails, IRC conversations, scientific journal articles or weblog postings. These models are applied to the detection of buzz (i.e. terms that occur with a higher-than-normal frequency) in them, which has attracted a lot of interest in the online world with the increasing number of periodic content producers. That is why in this paper we have used this kind of online sequences to test our system, though it is also valid for other types of event sequences. The algorithm assigns frequencies (number of events per time unit) to time intervals so that it produces an optimal fit to the data. The optimization procedure is a trade off between accurately fitting the data and avoiding too many frequency changes, thus overcoming the noise inherent in these sequences. This process has been traditionally performed using dynamic programming algorithms, which are limited by memory and efficiency requirements. This limitation can be a problem when dealing with long sequences, and suggests the application of alternative search methods with some degree of uncertainty to achieve tractability, such as the evolutionary algorithm proposed in this paper. This algorithm is able to reach the same solution quality as those classical dynamic programming algorithms, but in a shorter time. We also test different cost functions and propose a new one that yields better fits than the one originally proposed by Kleinberg on real-world data. Finally, several distributions of states for the finite state automata are tested, with the result that an uniform distribution produces much better fits than the geometric distribution also proposed by Kleinberg. We also present a variant of the evolutionary algorithm, which achieves a fast fit of a sequence extended with new data, by taking advantage of the fit obtained for the original subsequence.     

PARALLEL AND DISTRIBUTED EVOLUTIONARY COMPUTATION FOR FINANCIAL APPLICATIONS

Abstract

A survey of two parallel evolutionary computation techniques is presented: the genetic algorithms and genetic programming methods. An application of this approach to the induction of trading models is presented for financial assets, which is known as a hard problem. This study analyses the potential of this approach and the benefit of parallelization.     

Practical performance models of algorithms in evolutionary program induction and other domains

Evolutionary computation techniques have seen a considerable popularity as problem solving and optimisation tools in recent years. Theoreticians have developed a variety of both exact and approximate models for evolutionary program induction algorithms. However, these models are often criticised for being only applicable to simplistic problems or algorithms with unrealistic parameters. In this paper, we start rectifying this situation in relation to what matters the most to practitioners and users of program induction systems: performance. That is, we introduce a simple and practical model for the performance of program-induction algorithms. To test our approach, we consider two important classes of problems — symbolic regression and Boolean function induction — and we model different versions of genetic programming, gene expression programming and stochastic iterated hill climbing in program space. We illustrate the generality of our technique by also accurately modelling the performance of a training algorithm for artificial neural networks and two heuristics for the off-line bin packing problem.We show that our models, besides performing accurate predictions, can help in the analysis and comparison of different algorithms and/or algorithms with different parameters setting. We illustrate this via the automatic construction of a taxonomy for the stochastic program-induction algorithms considered in this study. The taxonomy reveals important features of these algorithms from the performance point of view, which are not detected by ordinary experimentation.     

广义人工生命的科学基础(I):工程技术基础

根据广义人工生命研究的特点,作者把系统论、控制论、信息论、人工智能、元胞自动机、L-系统、遗传算法、进化策略、进化规划、耗散结构理论、协同学、突变论、混沌、分形、转基因技术、克隆技术等的研究成果视为人工生命研究的主要科学基础。文章力求对元胞自动机、L-系统、遗传算法、进化策略、进化规划、耗散结构理论、协同学、突变论、混沌、分形和它们与人工生命的关系做系统而扼要的评述,为人工生命研究提供方便。