2008 Special issue

Operating mode scheduling is crucial for the lifetime of wireless sensor networks (WSNs). However, the growing scale of networks has made such a scheduling problem more challenging, as existing set cover and evolutionary algorithms become unable to provide satisfactory efficiency due to the curse of dimensionality. In this paper, a Kuhn–Munkres (KM) parallel genetic algorithm is developed to solve the set cover problem and is applied to the lifetime maximization of large-scale WSNs. The proposed algorithm schedules the sensors into a number of disjoint complete cover sets and activates them in batch for energy conservation. It uses a divide-and-conquer strategy of dimensionality reduction, and the polynomial KM algorithm a are hence adopted to splice the feasible solutions obtained in each subarea to enhance the search efficiency substantially. To further improve global efficiency, a redundant-trend sensor schedule strategy was developed. Additionally, we meliorate the evaluation function through penalizing incomplete cover sets, which speeds up convergence. Eight types of experiments are conducted on a distributed platform to test and inform the effectiveness of the proposed algorithm. The results show that it offers promising performance in terms of the convergence rate, solution quality, and success rate.

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Kuhn–Munkres Parallel Genetic Algorithm for the Set Cover Problem and Its Application to Large-Scale Wireless Sensor Networks

Gait generation is very important as it directly affects the quality of locomotion of legged robots. As this is an optimization problem with constraints, it readily lends itself to Evolutionary Computation methods and solutions. This paper reviews the techniques used in evolution-based gait optimization, including why Evolutionary Computation techniques should be used, how fitness functions should be composed, and the selection of genetic operators and control parameters. This paper also addresses further possible improvements in the efficiency and quality of evolutionary gait optimization, some problems that have not yet been resolved and the perspectives for related future research.

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A review of gait optimization based on evolutionary computation

The max-cut problem is one of well-known NP-complete problems and has applications in various fields such as the design process for VLSI (Very-Large-Scale Integration) chips and spin glass theory in statistical physics. In this paper, a harmony search algorithm for the max-cut problem is proposed. Compared to genetic algorithm, harmony search algorithm has advantages of generating a new vector after considering all of the existing vectors and requiring only a few number of parameters to be determined before the run of the algorithm. For 31 benchmark graphs of various types, the proposed harmony search algorithm is compared with a newly developed genetic algorithm and another genetic algorithm taken from the recent literature are presented. The proposed harmony search algorithm produced significantly better results than the two genetic algorithms.

A comparison study of harmony search and genetic algorithm for the max-cut problem

Estimating the non-linear dynamics of free-flying objects

This paper introduces a new approach to developing a fast gait for a quadruped robot using genetic programming (GP). Planning gaits for legged robots is a challenging task that requires optimizing parameters in a highly irregular and multi-dimensional space. Several recent approaches have focused on using genetic algorithms (GAs) to generate gaits automatically and have shown significant improvement over previous gait optimization results. Most current GA-based approaches optimize only a small, pre-selected set of parameters, but it is difficult to decide which parameters should be included in the optimization to get the best results. Moreover, the number of pre-selected parameters is at least 10, so it can be relatively difficult to optimize them, given their high degree of interdependence. To overcome these problems of the typical GA-based approach, we have proposed a seemingly more efficient approach that optimizes joint trajectories instead of locus-related parameters in Cartesian space, using GP. Our...

Genetic Programming-Based Automatic Gait Generation in Joint Space for a Quadruped Robot

The encoding or representation scheme in evolutionary algorithms is very important because it can greatly affect their performance. Most previous evolutionary algorithms for solving graph problems have traditionally used a vertex-based encoding in which each gene corresponds to a vertex. In this paper, addressing the well-known maximum cut problem, we introduce an edge-set encoding based on the spanning tree—a kind of edge-based encoding. In our encoding scheme, each gene corresponds to an edge subset derived from a spanning tree. In contrast to a traditional edge-based encoding in which each gene corresponds to only one edge, our encoding scheme has the advantage of representing only feasible solutions, so there is no need to apply a repair step. We present a genetic algorithm based on this new encoding. We have conducted various experiments on a large set of test graphs including commonly used benchmark graphs and have obtained performance improvement on sparse graphs, which frequently appear in real-world applications such as social networks and systems biology, in comparison with a scheme using a vertex-based encoding.

An Edge-Set Representation Based on a Spanning Tree for Searching Cut Space

This paper introduces a new approach to develop fast gait for quadruped robot using genetic programming (GP). Several recent approaches have focused on using genetic algorithm (GA) to generate gait automatically and shown significant improvements over previous results. Most of current GA based approaches use pre-selected parameters, but it is difficult to select the appropriate parameters for the optimization of gait. To overcome these problems of GA based approach, we proposed an efficient approach which optimizes joint angle trajectories using genetic programming. Our GP based method has obtained much better results than GA based approaches for experiments of Sony AIBO ers-7 in Webots environment. The elite archive mechanism(EAM) was introduced to prevent premature convergence problems in GP and has shown improvements.

/pdf/genetic-programming-based-automatic-gait-generation-for-38q2b1dvoj.pdf

Genetic programming based automatic gait generation for quadruped robots

Most of previous genetic algorithms for solving graph problems have used vertex-based encoding. In this paper, we introduce spanning tree-based encoding instead of vertex-based encoding for the well-known MAX CUT problem. We propose a new genetic algorithm based on this new type of encoding. We conducted experiments on benchmark graphs and could obtain performance improvement on sparse graphs, which appear in real-world applications such as social networks and systems biology, when the proposed methods are compared with ones using vertex-based encoding.

A spanning tree-based encoding of the MAX CUT problem for evolutionary search

Planning gaits for legged robots is a challenging task that requires optimizing parameters in a highly irregular and multidimensional space. Two gait generation methods using GA (Genetic Algorithm), GP (genetic programming) are compared to develop fast locomotion for a quadruped robot. GA-based approaches seek to optimize a pre-selected set of parameters which include locus of paw and stance parameters of initial position. A GP-based technique is an effective way to generate a few joint trajectories instead of the locus of paw positions and many stance parameters. Optimizations for two proposed methods are executed and analyzed using a Webots simulation of the quadruped robot built by Bioloid. Furthermore, simulation results for the two proposed methods are tested in a real quadruped robot, and the performance and motion features of GA-, GP -based methods are compared.

Soohwan Hyun

Papers

Genetic Programming-Based Automatic Gait Generation in Joint Space for a Quadruped Robot

An Edge-Set Representation Based on a Spanning Tree for Searching Cut Space

Genetic programming based automatic gait generation for quadruped robots

A spanning tree-based encoding of the MAX CUT problem for evolutionary search

A comparative study between genetic algorithm and genetic programming based gait generation methods for quadruped robots