Ali M. S. Zalzala

Recent developments in evolutionary computation for manufacturing optimization: problems, solutions, and comparisons

The use of intelligent techniques in the manufacturing field has been growing the last decades due to the fact that most manufacturing optimization problems are combinatorial and NP hard. This paper examines recent developments in the field of evolutionary computation for manufacturing optimization. Significant papers in various areas are highlighted, and comparisons of results are given wherever data are available. A wide range of problems is covered, from job shop and flow shop scheduling, to process planning and assembly line balancing.

Investigating the use of genetic programming for a classic one-machine scheduling problem

Abstract Genetic programming has rarely been applied to manufacturing optimisation problems. In this paper the potential use of genetic programming for the solution of the one-machine total tardiness problem is investigated. Genetic programming is utilised for the evolution of scheduling policies in the form of dispatching rules. These rules are trained to cope with different levels of tardiness and tightness of due dates.

A genetic approach to motion planning of redundant mobile manipulator systems considering safety and configuration

This paper presents a genetic algorithm approach to multi-criteria motion planning of a mobile manipulator system considering position and configuration optimisation. Travelling distance and path safety are considered in planning the motion of the mobile system. A wave front expansion algorithm is used to build the numerical potential fields for both the goal and obstacles by representing the workspace as a grid. The unsafeness of a grid point is defined as the numerical potential produced by obstacles. For multi-criteria position and configuration optimisation, obstacle avoidance, least torque norm, manipulability and torque distribution are considered. The emphasis is put on using genetic algorithms to search for global optimum and solve the minimax problem for torque distribution. Various simulation results from two examples show that the proposed genetic algorithm approach performs better than conventional methods.

A genetic rule-based data clustering toolkit

Clustering is a hard combinatorial problem and is defined as the unsupervised classification of patterns. The formation of clusters is based on the principle of maximizing the similarity between objects of the same cluster while simultaneously minimizing the similarity between objects belonging to distinct clusters. This paper presents a tool for database clustering using a rule-based genetic algorithm (RBCGA). RBCGA evolves individuals consisting of a fixed set of clustering rules, where each rule includes d non-binary intervals, one for each feature. The investigations attempt to alleviate certain drawbacks related to the classical minimization of square-error criterion by suggesting a flexible fitness function which takes into consideration, cluster asymmetry, density, coverage and homogeny.

Genetic control of near time-optimal motion for an industrial robot arm

The search for the minimum-time path of a robotic manipulator graphically by tessellating the joint space involves heavy computational burden. Genetic algorithms (GAs) have been used to tackle this problem and have reduced the computational search time a great deal. The work presented here provides a practical GAs motion control for possible real-time implementations. A kind of heuristic search technique is found to be necessary to further reduce the search time. The end-velocity is dealt with by placing a penalty on the objective function. A scheme using a fifth order polynomial function to generate smooth initial path population is presented. Much better results with much less cost have been acquired than many of those reported earlier in the literature. Time-optimal motion for a six DOF robot arm is also included, which is made possible by using GAs. It has been found that the quickest motion is not necessarily a straight line in joint space.

NOCEA: A rule-based evolutionary algorithm for efficient and effective clustering of massive high-dimensional databases

Clustering is a descriptive data mining task aiming to group the data into homogeneous groups. This paper presents a novel evolutionary algorithm (NOCEA) that efficiently and effectively clusters massive numerical databases. NOCEA evolves individuals of variable-length consisting of disjoint and axis-aligned hyper-rectangular rules with homogeneous data distribution. The antecedent part of the rules includes an interval-like condition for each dimension. A novel quantisation algorithm imposes a regular multi-dimensional grid structure onto the data space to reduce the search combinations. Due to quantisation the boundaries of the intervals are encoded as integer values. The evolutionary search is guided by a simple data coverage maximisation function. The enormous data space is effectively explored by task-specific recombination and mutation operators producing candidate solutions with no overlapping rules. A parsimony generalisation operator shortens the discovered knowledge by replacing adjacent rules with more generic ones. NOCEA employs a special homogeneity operator that enforces quasi-uniform data distribution in the space enclosed by the candidate rules. After convergence the discovered knowledge undergoes simplification to perform subspace clustering, and to assemble the clusters. Results using real-world datasets are included to show that NOCEA has several attractive properties for clustering including: (a) comprehensible output in the form of disjoint and homogeneous rules, (b) the ability to discover clusters of arbitrary shape, density, size, and data coverage, (c) ability to perform effective subspace clustering, (d) near linear scalability with the database size, data and cluster dimensionality, and (e) substantial potential for task parallelism (speedup of 13.8 on 16 processors). A real-world example is a detailed study of the seismicity along the African-Eurasian-Arabian plate boundaries.

A genetic programming heuristic for the one-machine total tardiness problem

Genetic programming has rarely been applied to manufacturing optimisation problems. In this report we investigate the potential use of genetic programming for the solution of the one-machine total tardiness problem. Combinations of dispatching rules are employed as an indirect way of representing permutations within a modified genetic programming framework. Hybridisation of genetic programming with local search techniques is also introduced, in an attempt to improve the quality of solutions. All the algorithms are tested on a large number of benchmark problems with different levels of tardiness and tightness of due dates.

Near Time-Optimal Collision-Free Motion Planning of Robotic Manipulators Using an Evolutionary Algorithm

A technique for open-loop minimum time planning of time-histories of control torques for robotic manipulators subject to constraints on the control torques using evolutionary algorithm is presented here. Planning is carried out in joint space of the manipulator and the path is represented as a string of via-points connected by cubic spline polynomial functions. Repeated path modification is done by using the evolutionary algorithm to search for a time-optimal path. Time taken to traverse over a particular path is calculated by reducing the dynamic equations of motion over that path in terms of a path parameter and then calculating the time optimal-control over that path. This time is included in the fitness function of the evolutionary algorithm to guide the search for the time-optimal trajectory. Simulation results for 2-DOF and 3-DOF dual-arm systems are presented.

Towards an evolutionary neural network for gait analysis

This paper presents initial investigations into an evolutionary neural network suitable for gait analysis of human motion. The approach here is to develop an intelligent black box that can take the physiological signals (EMG) and interpret them to give accurate information on the position and movement of the knee (gait). Two MLP networks are presented with weight evolving algorithms employing mutation and crossover separately. Simulation results show the evolutionary algorithms exhibiting particularly better ability to generalise solutions, which is an important aspect for the reliability of EMG/gait map generation.

Towards effective subspace clustering with an evolutionary algorithm

We propose a new evolutionary algorithm for subspace clustering in very large and high-dimensional databases. The design includes task-specific coding and genetic operators, along with a nonrandom initialization procedure. Experimental results show that the algorithm scales almost linearly with the size and dimensionality of the database as well as the dimensionality of the hidden clusters. Our algorithm is able to discover clusters of different densities embedded in both low and high dimensional subspaces of the original space. Finally, the discovered knowledge is presented in the form of nonoverlapping clustering rules where only those features relevant to the clustering are reported. These two properties contributes to the relatively high comprehensibility of the clustering output.