Lawrence Davis

Evolutionary Computation in Practice

This book is loaded with examples in which computer scientists and engineers have used evolutionary computation - programs that mimic natural evolution - to solve real problems. They aren t abstract, mathematically intensive papers, but accounts of solving important problems, including tips from the authors on how to avoid common pitfalls, maximize the effectiveness and efficiency of the search process, and many other practical suggestions. Some of the authors have already won Humies - Human Competitive Results Awards - for the work described in this book. I highly recommend it as a highly concentrated source of good problem-solving approaches that are applicable to many real-world problems.

AN INTRODUCTION TO EVOLUTIONARY COMPUTATION IN PRACTICE

Deploying Evolutionary Computation (EC) solutions to real-world problems involves a wide spectrum of activities, ranging from framing the business problems and implementing the solutions to the final deployment of the solutions to the field. However, issues related to these activities are not commonly discussed in a typical EC course curriculum. Meanwhile, although the values of applied research are acknowledged by most EC technologists, the perception seems to be very narrow: success stories boost morale and high profile applications can help to secure funding for future research and can help to attract high caliber students. In this book, we compiled papers from practitioners of EC with the following two purposes: Demonstrating applied research is essential in order for EC to remain a viable science. By applying EC techniques to important unsolved/or poorlysolved real-world problems, we can validate a proposed EC method and/or identify its weakness that restricts its applicability. Providing information on transferring EC technology to real-world problem solving and successful deployment of the solutions to the field. 1. APPLIED RESEARCH

Reducing costs of backhaul networks for PCS networks using genetic algorithms

Designing cost-effective telecommunications networks often involves solving several challenging, interdependent combinatorial optimization problems simultaneously. For example, it may be necessary to select a least-cost subset of locations (network nodes) to serve as hubs where traffic is to be aggregated and switched; optimally assign other nodes to these hubs, meaning that the traffic entering the network at these nodes will be routed to the assigned hubs while respecting capacity constraints on the links; and optimally choose the types of links to be used in interconnecting the nodes and hubs based on the capacities and costs associated with each link type. Each of these three combinatorial optimization problems must be solved while taking into account its impacts on the other two. This paper introduces a genetic algorithm (GA) approach that has proved effective in designing networks for carrying personal communications services (PCS) traffic. The key innovation is to represent information about hub locations and their interconnections as two parts of a chromosome, so that solutions to both aspects of the problem evolve in parallel toward a globally optimal solution. This approach allows realistic problems that take 4–10 hours to solve via a more conventional branch-and-bound heuristic to be solved in 30–35 seconds. Applied to a real network design problem provided as a test case by Cox California PCS, the heuristics successfully identified a design 10% less expensive than the best previously known design. Cox California PCS has adopted the heuristic results and plans to incorporate network optimization in its future network designs and requests for proposals.

Dynamic Hierarchical Packing of WirelessSwitches Using a Seed, Repair and Replace Genetic Algorithm

Suppose that items of equipment are to be added to a supply station (e.g., new switch modules are to be added to a telecommunications switch) over time to meet growing demand requirements. Both supply and demand have multiple components: an item of equipment supplies different amounts of several resources, and demand may be expressed in terms of the vector of resources required. There are several different types of equipment to choose among, each type supplying known amounts of each resource per unit of equipment. The supply station is organized into bays, shelves, or other capacitated “containers” so that when the cumulative amount of equipment added exceeds the holding capacity of the installed containers, new containers must be added, creating a relatively large jump in cumulative costs. Thus, it is desirable to sequentially “pack” items of equipment into the available containers, by choosing which types of equipment to install when, so as to minimize the total cost of covering demand in each period. We discuss an instance of this problem arising from wireless telephony and describe the performance of a conventional branch-and-bound optimization algorithm for solving it. The branch-and-bound approach works well on small instances of the problem, and has been used successfully in practical planning. However, it can take CPU-days to run, thus preventing development of a useful interactive planning tool. Therefore, we introduce a novel “seed, repair, and replace” genetic algorithm (SRR-GA) for solving dynamic packing problems of this type. We contrast its performance with the branch-and-bound algorithms on both hand-generated and randomly-created dynamic packing problems, finding that the SRR-GA is two to three orders of magnitude faster and produces solutions of equal or better quality on practical problems. Variations of the dynamic packing problem and of the SRR-GA for solving it are mentioned, and the paper concludes by suggesting other potential applications of the SRR-GA to hard combinatorial optimization problems.

Evolutionary Computation Applications: Twelve Lessons Learned

A liquid transfer rotary valve assembly capable of measuring and delivering at least a pair of different sample volumes in the microliter range along with a predetermined volume of diluent as a pair of different dilutions directed to different locations. The valve assembly provided herein includes a pair of stationary outer disc members and a movable inner disc member sandwiched therebetween, the facing surface portions being sealingly frictionally engaged. Each disc has an axial central passage to accommodate a spindle. An external hollow loop is provided on one of the stationary discs having a precise volume in the microliter range. A segmenting passageway is provided on the movable inner disc member. A source of sample is communicatively coupled through the other stationary disc to the segmenting passage which is arranged in series with the external loop. After a continuous stream of sample is disposed through the serially connected segmenting passage and loop, the inner member is angularly moved to place the content of the segmenting passage in communication with a source of diluent and a passage leading exterior of the valve. Reverse angular movement of the valve element enables backwash through the valve subsequent to delivery.