Search

Artificial bee colony (ABC) algorithm has attracted growing interest for the continuous global optimization problems (CGOPs), where numerous algorithmic extensions have been developed. However, existing studies generally employ identical population size to perform the comparison among different ABC variants, regardless a fact that the generally suitable population size should be algorithm-dependent. Here we focus on the analysis of population size. This study is conducted in several well-known ABC variants under a set of benchmark CGOPs. We demonstrate that i) with the independently optimal population size, standard ABC can perform competitively comparing with its advanced variants, and ii) the most remunerative population size is related to the algorithmic exploitation/exploration ability. We anticipate that this study will provide useful insights to guide the appropriate usage of ABC, as well as its further enhancements.

In this paper, we integrate variable neighborhood search (VNS) into artificial bee colony (ABC) algorithm so that the search ability under variable neighborhood structures and local search is accelerated. Two VNS methods, including the reduced VNS and basic VNS, are integrated to develop different versions. The proposed algorithm, named variable neighborhood ABC (VNABC), is verified in a comprehensive set of benchmark functions. The experimental results confirm that VNABC outperforms the state-of-the-art ABC and differential evolution (DE) algorithms in terms of the convergence speed.

As a relatively new population-based technique for the continuous optimization problems, artificial bee colony algorithm (ABC) has verified its superiority and robustness over traditional evolutionary algorithms. However, regarding the wide variety of ABC extensions, the use of search experience produced during the search progress has been relatively unexplored to improve the performance. This results in an enormous waste of search resource to lower the search efficiency. In this paper, we develop a search experience-based search adaptation (SESA) approach to accelerate the search efficiency of ABC by taking three search resources into account, that is, the successful search directions, the fitness resource and the large number of failed solutions. The experiments over a comprehensive set of benchmark functions demonstrate that SESA can significantly accelerate the search efficiency of ABC.

Artificial bee colony (ABC) algorithm is a relatively new optimization technique that simulates the intelligent foraging behavior of honey bee swarms. It has been applied to several optimization domains to show its efficient evolution ability. In this paper, ABC algorithm is applied for the first time to evolve a directed graph chromosome structure, which derived from a recent graph-based evolutionary algorithm called genetic network programming (GNP). Consequently, it is explored to new application domains which can be efficiently modeled by the directed graph of GNP. In this work, a problem of controlling the agents's behavior under a wellknown benchmark testbed called Tileworld are solved using the ABC-based evolution strategy. Its performance is compared with several very well-known methods for evolving computer programs, including standard GNP with crossover/mutation, genetic programming (GP) and reinforcement learning (RL).

Though there are numerous approaches developed currently, exploring the practical applications of estimation of distribution algorithm (EDA) has been reported to be one of the most important challenges in this field. This paper is dedicated to extend EDA to solve one of the most active research problems - stock trading, which has been rarely revealed in the EDA literature. A recent proposed graph-based EDA called reinforced probabilistic model building genetic network programming (RPMBGNP) is investigated to create stock trading rules. With its distinguished directed graph-based individual structure and the reinforcement learning-based probabilistic modeling, we demonstrate the effectiveness of RPMBGNP for the stock trading task through real-market stock data, where much higher profits are obtained than traditional non-EDA models.

Traditional evolutionary algorithms (EAs) generally starts evolution from scratch, in other words, randomly. However, this is computationally consuming, and can easily cause the instability of evolution. In order to solve the above problems, this paper describes a new method to improve the evolution efficiency of a recently proposed graph-based EA - genetic network programming (GNP) - by introducing knowledge transfer ability. The basic concept of the proposed method, named GNP-KT, arises from two steps: First, it formulates the knowledge by discovering abstract decision-making rules from source domains in a learning classifier system (LCS) aspect; Second, the knowledge is adaptively reused as advice when applying GNP to a target domain. A reinforcement learning (RL)-based method is proposed to automatically transfer knowledge from source domain to target domain, which eventually allows GNP-KT to result in better initial performance and final fitness values. The experimental results in a real mobile robot control problem confirm the superiority of GNP-KT over traditional methods.

Genetic Network Programming (GNP) is derived from Genetic Algorithm (GA) and Genetic Programming (GP), which applies evolution theory to evolve a population of directed graph to model complex systems. It has been shown that GNP can solve typical control problems, as well as many real-world problems. However, studying GNP is mainly focused on the specific aspect, while the fundamental characteristics that ensure the success of GNP are rarely investigated in the previous research. This paper reveals an important feature of GNP - reusability of nodes - to efficiently identify and formulate the building blocks of evolution. Accordingly, adaptive GNP is developed which self-adapts both crossover and mutation probabilities of each search variable to circumstances. The adaptation allows the automatic adjustment of evolution bias toward the frequently reused nodes in high-quality individuals. The adaptive GNP is compared with traditional GNP in a benchmark control testbed to evaluate its superiority.

Recent advances in Learning Classifier Systems (LCSs) have shown their sequential decision-making ability with a generalization property. In this paper, a novel LCS named extended rule-based Genetic Network Programming (XrGNP) is proposed. Different from most of the current LCSs, the rules are represented and discovered through a graph-based evolutionary algorithm GNP, which consequently has the distinct expression ability to model and evolve the decision-making rules. XrGNP is described in details in which its unique features are explicitly mapped. Experiments on benchmark and real-world multi-step problems demonstrate the effectiveness of XrGNP.

This paper presents an algorithm to discover and reuse subroutines in Variable Size Genetic Network Programming (GNPvs) called Subroutine embedded GNPvs (SGNPvs). GNPvs is a general type of GNP, which has a direct graph representation with changeable size. In order to improve the performance of GNPvs, SGNPvs has been proposed, in which a subroutine mechanism has been introduced to GNPvs by module acquisition. In SGNPvs, useful subgraphs are extracted and reused for individuals. Through extracting new subroutines to replace the old subroutines, SGNPvs can evolve the subroutines as well as evolve the individuals. The simulation results verify the performance of SGNPvs on a well-known dynamic multi-agent test bed - Tileworld.

A novel graph-based Estimation of Distribution Algorithm (EDA) named Probabilistic Model Building Genetic Network Programming (PMBGNP) has been proposed. Inspired by classical EDAs, PMBGNP memorizes the current best individuals and uses them to estimate a distribution for the generation of the new population. However, PMBGNP can evolve compact programs by representing its solutions as graph structures. Therefore, it can solve a range of problems different from conventional ones in EDA literature, such as data mining and Reinforcement Learning (RL) problems. This paper extends PMBGNP from discrete to continuous search space, which is named PMBGNP-AC. Besides evolving the node connections to determine the optimal graph structures using conventional PMBGNP, Gaussian distribution is used for the distribution of continuous variables of nodes. The mean value μ and standard deviation σ are constructed like those of classical continuous Population-based incremental learning (PBILc). However, a RL technique, i.e., Actor-Critic (AC), is designed to update the parameters (μ and σ). AC allows us to calculate the Temporal-Difference (TD) error to evaluate whether the selection of the continuous value is better or worse than expected. This scalar reinforcement signal can decide whether the tendency to select this continuous value should be strengthened or weakened, allowing us to determine the shape of the probability density functions of the Gaussian distribution. The proposed algorithm is applied to a RL problem, i.e., autonomous robot control, where the robot's wheel speeds and sensor values are continuous. The experimental results show the superiority of PMBGNP-AC comparing with the conventional algorithms.

This paper proposes a different type of Genetic Network Programming (GNP) — Variable Size Genetic Network Programming (GNPvs) with Binomial Distribution. In contrast to the individuals with fixed size in Standard GNP, GNPvs will change the size of the individuals and obtain the optimal size of them during evolution. The proposed method defines a new type of crossover to implement the new feature of GNP. The new crossover will select the number of nodes to move from each parent GNP to another parent GNP. The probability of selecting the number of nodes to move satisfies the binomial probability distribution. The proposed method can keep the effectiveness of crossover and improve the performance of GNP. In order to verify the performance of the proposed method, a well-known benchmark problem — Tile-world is used in the simulations. The simulation results show the effectiveness of the proposed method.

This paper proposed a novel EDA, where a directed graph network is used to represent its chromosome. In the proposed algorithm, a probabilistic model is constructed from the promising individuals of the current generation using reinforcement learning, and used to produce the new population. The node connection probability is studied to develop the probabilistic model, therefore pairwise interactions can be demonstrated to identify and recombine building blocks in the proposed algorithm. The proposed algorithm is applied to a problem of agent control, i.e., autonomous robot control. The experimental results show the superiority of the proposed algorithm comparing with the conventional algorithms.

As an extension of GA and GP, a new evolutionary algorithm named Genetic Network Programming (GNP) has been proposed. GNP uses the directed graph structure to represent its solutions, which can express the dynamic environment efficiently. The reusable nodes of GNP can construct compact structures, leading to a good performance in complex problems. In addition, a probabilistic model building GNP named GNP with Estimation of Distribution Algorithm (GNP-EDA) has been proposed to improve the evolution efficiency. GNP-EDA outperforms the conventional GNP by constructing a probabilistic model by estimating the probability distribution from the selected elite individuals of the previous generation. In this paper, a probabilistic model building GNP with multiple probability vectors (PMBGNP M ) is proposed. In the proposed algorithm, multiple probability vectors are used in order to escape from premature convergence, and genetic operations like crossover and mutation are carried out to the probability vectors to maintain the diversities of the populations. The proposed algorithm is applied to the controller of autonomous robots and its performance is evaluated.

The optimal route search in car navigation Systems is often considered to be a route search from the origin to destination. Many algorithms have been proposed to search for the optimal route from the origin to destination. However, in real situations several restrictions may need to be considered in the route search like some intersections must be included in the route while some should be excluded. The conventional optimal route search methods cannot consider such restrictions in the route search. In this paper, we propose a method to find the optimal route considering such restrictions, focusing on the restriction that some intermediate destinations must be visited before reaching the final destination. The proposed method is divided into three steps. In the first step, the optimal traveling times among the origin, intermediate destinations and final destination are calculated. In the second step, the optimal order of visiting intermediate destinations is optimized using RasID-D, a random search method for discrete optimization problems. Finally, in the third step, the optimal route from the origin to destination via intermediate destinations is determined. The paper also discusses the heuristic initialization to increase the efficiency of the optimal search. The proposed method was evaluated using a grid network with randomly generated intermediate intersections. Simulation results showed that the proposed method is more efficient than the genetic algorithm for optimizing the visiting order.

Time Related Association rule mining is a kind of sequence pattern mining for sequential databases. In this paper, a method of Generalized Association Rule Mining using Genetic Network Programming (GNP) with MBFP(Multi-Branch and Full-Pathes) processing mechanism has been introduced in order to find time related sequential rules more efficiently. GNP represents solutions as directed graph structures, thus has compact structure and partially observable Markov decision process. GNP has been applied to generate time related candidate association rules as a tool using the database consisting of a large number of time related attributes. The aim of this algorithm is to better handle association rule extraction from the databases in a variety of time-related applications, especially in the traffic volume prediction and its usage. The generalized algorithm which can find the important time related association rules has been proposed and experimental results are presented considering how to use the rules to predict the future traffic volume and also how to use the traffic prediction in the optimal search problem.