Roger L. Wainwright

Autonomous local path planning for a mobile robot using a genetic algorithm

This work presents results of our work in development of a genetic algorithm based path-planning algorithm for local obstacle avoidance (local feasible path) of a mobile robot in a given search space. The method tries to find not only a valid path but also an optimal one. The objectives are to minimize the length of the path and the number of turns. The proposed path-planning method allows a free movement of the robot in any direction so that the path-planner can handle complicated search spaces.

Genetic algorithms for autonomous robot navigation

Engineers and scientists use instrumentation and measurement equipment to obtain information for specific environments, such as temperature and pressure. This task can be performed manually using portable gauges. However, there are many instances in which this approach may be impractical; when gathering data from remote sites or from potentially hostile environments. In these applications, autonomous navigation methods allow a mobile robot to explore an environment independent of human presence or intervention. The mobile robot contains the measurement device and records the data then either transmits it or brings it back to the operator. Sensors are required for the robot to detect obstacles in the navigation environment, and machine intelligence is required for the robot to plan a path around these obstacles. The use of genetic algorithms is an example of machine intelligence applications to modern robot navigation. Genetic algorithms are heuristic optimization methods, which have mechanisms analogous to biological evolution. This article provides initial insight of autonomous navigation for mobile robots, a description of the sensors used to detect obstacles and a description of the genetic algorithms used for path planning.

A genetic algorithm for packing in three dimensions

Recent research in Bin Packing haa almost exclusively been in two dimensions. In this paper we extend the classic Bin Packing problem to three dimensions. We investigate the solutions for the three dmenaicmal packing problem using fust fit ond next fit packing strategies with and without genetic algorithms. Five data sets were used to test our algorithms, both random and contrived. They range from 50 to 500 packages. We also studied several existing crossover hmctions for the genetic algorithm: PMX, Cycle, and 0rder2. A new crossover function, Randl, is presented. The genetic algorithm was tested using a randomly generated initial population POOLand a srded initial pool. The seeded pool was generated from a package ordering produced by rotating and sorting the packages by decreasing height. Our results show the seeded genetic algorithm using Next Fit and PMX produced the best overall results for the data sets tested. The see&d genetic algorithm using Next Fh and Gr&r2 provi&d the beat results considering both rapid execution time and packing efficiently. We found genetic algdhma to be art excellent technique for yielding good solutions for the three dimensional packing problem.

Applying genetic algorithms to the U-shaped assembly line balancing problem

The traditional assembly line balancing problem considers the manufacturing process of a product where production is specified in terms of a sequence of tasks that need to be assigned to workstations. Each task takes a known number of time units to complete. Also, precedence constraints exist among tasks: each task can be assigned to a station only after all its predecessors have been assigned to stations. The U-shaped assembly line balancing problem is a relatively new problem derived from the traditional assembly line balancing problem. In the U-shaped assembly line balancing problem, a task can be assigned to a station either after all of its predecessors or all of its successors have been assigned to stations. This paper presents a genetic algorithm (GA) solution to the Type I U-shaped assembly line balancing problem. Our research provides a global framework which can be used to deal with the two possible variations of this problem-minimizing the total idle time and balancing the workload among stations-or a combination of both. We developed six different assignment algorithms as a means for interpreting a chromosome and assigning tasks to workstations. The results show the GA to be an excellent technique for this problem. In 61 standard test cases from the literature, our GA obtained the same results as previous researchers in 49 cases, superior results in 11 cases, and in only one case did worse. Moreover, the GA proved to be computationally efficient.

LibGA: a user-friendly workbench for order-based genetic algorithm research

Over the years there haa been several packages developed that provide a workbench for genetic algorithm (GA) research. Most of these packages use the generational model inspired by GEN ESIS. A few have adopted the steady-state model used in Genitor. Unfortunately, they have some deficiencies when working with orderhsed problems such as packing, routing, and scheduling. This paper describes LibGA, which was developed specifically for order-baaed problems, but which also works easily with other kinds of problems. It offers an easy to use ‘user-friendly’ interface and allows comparisons to be made between both generational and steadystate genetic algorithms for a particular problem. It includes a variety of genetic operators for reproduction, crossover, and mutation. LibGA makes it easy to use these operators in new ways for particular applications or to develop and include new operators. Finally, it offers the unique new feature of a dynamic generation gap.

Placing Text Labels on Maps and Diagrams using Genetic Algorithms with Masking

Cartographic label placement is one of the most time-consuming tasks in the production of high quality maps and other high quality graphical displays. It is essential that text labels used to identify various features and objects be placed in a clear and unobscured manner. In this article we are concerned with the placement of labels for point features. Specifically, the point feature label placement (PFLP) problem is the problem of placing text labels to point features on a map, graph, or diagram in such a manner so as to maximize legibility. The PFLP problem has been shown to be NP-hard. We propose a heuristic method for the PFLP problem based on genetic algorithms (GA), an adaptive, robust, search and optimization technique based on the principles of natural genetics and survival of the fittest. In particular we emphasize the notion of masking to preserve optimal subsequences in chromosomes and prevent their disruption during crossover and mutation. We ran our algorithms on randomly placed point features in a region, and on datasets from various regions of the USA map with great success. Our GA Implementation with masking solved each of the test cases extremely well, and proved to be an excellent heuristic for solving the PFLP problem. Furthermore, our GA with masking performed significantly better than other PFLP algorithms from the literature.

Designing telecommunications networks using genetic algorithms and probabilistic minimum spanning trees

In this paper we investigate genetic algorithms (GA) as a .heuristic technique for obtaining near optimal solutions to the probabilistic minimum spanning tree (PMST) problem. The PMST problem is a natural generalizat ion of the classical minimum spanning tree (MST) problem and is frequently a more realistic model. The PMST problem addresses the c i r c ums t ances that a r i se when not all nodes are deterministically present but, rather, nodes are present with known probabilities. Although there are some special cases that are solvable in polynomial time, it is known that the PMST problem is NP-complete.

A class of sorting algorithms based on Quicksort

Bsort, a variation of Quicksort, combines the interchange technique used in Bubble sort with the Quicksort algorithm to improve the average behavior of Quicksort and eliminate the worst case situation of O(n2) comparisons for sorted or nearly sorted lists. Bsort works best for nearly sorted lists or nearly sorted in reverse.

Dynamic scheduling of computer tasks using genetic algorithms

We concentrate on non-preemptive hard real-time scheduling algorithms. We compare FIFO, EDLF, SRTF and genetic algorithms for solving this problem. The objective of the scheduling algorithm is to dynamically schedule as many tasks as possible such that each task meets its execution deadline, while minimizing the total delay time of all of the tasks. We present a MicroGA that uses a small population size of 10 chromosomes, running for 10 trials using a rather high mutation rate with a sliding window of 10 tasks. The steady-state GA was determined to be better than the generational GA for our MicroGA. We also present a parallel MicroGA model designed for parallel processors. The parallel MicroGA works best when migration is used to move tasks from one processor to another to even out the load as much a possible. Test cases show that the sequential MicroGA model and the parallel MicroGA model produced superior task schedules compared to other algorithms tested.<<ETX>>

Solving combinatorial optimization problems using parallel simulated annealing and parallel genetic algorithms

There are many combinatorial optimization problems for which there exists no director efficient method of solution. Simtdated annealtig (SA) and genetic algorithms (GA) are two promisirtg techniques for solving large optimization problems. The authorx have developed a parallel simulated annealing algorithm and a parallel genetic algorithm for a hypercube multiprocessor system. To compare the performance of these algorithms, we investigated two representative combinatorial optimization problems, the Traveling Salesman Problem (TSP) and the onedimensionrd Package Placement Problem (PPP). The parallel gestetic algorithm performed consistently better than the parallel simulated annealing algorithm in all of the cases tested. In addition, we tested five crossover functions on the sequential genetic algorithm for the Package Placement Problem and determined in every case the edge recombination crossover function was superior. There are some significant differences between genetic algorithms and simulated annealing that may account for the superior performanw of the parallel genetic algorithm for these ty~s of problems. We found it fairly easy to fine tune the parameters that drive a parallel GA for near optimal performance (population size, migration rate, and migration interval) compared to the parameters that drive a parallel simulated annealing algorithm. Furthermore, our parallel genetic algorithm is more mature than our newly developed parallel simulated annealing algorithm, Several future enhancements to the parallel simulated annealing algorithm are presented. “ Research partially supported by OCAST Grant ARO-038 md Sun Microsystems, Inc. Permission to copy without fee all or part of this material is grantad providad that the copias ara not mada or distributed for direct commercial advantage, tha ACM copyright notica and tha title of the publication and its date appear, and notice is given that copying ia by permission of tha Association for Computing Machinery. To copy otherwise, or to rapublish, requires a fae and/or specific permission. 01992 ACM O-89791-502-X/9210002/1031 ...