: This research organizes, presents, and analyzes contemporary Multiobjective Evolutionary Algorithm (MOEA) research and associated Multiobjective Optimization Problems (MOPs). Using a consistent MOEA terminology and notation, each cited MOEAs' key factors are presented in tabular form for ease of MOEA identification and selection. A detailed quantitative and qualitative MOEA analysis is presented, providing a basis for conclusions about various MOEA-related issues. The traditional notion of building blocks is extended to the MOP domain in an effort to develop more effective and efficient MOEAs. Additionally, the MOEA community's limited test suites contain various functions whose origins and rationale for use are often unknown. Thus, using general test suite guidelines appropriate MOEA test function suites are substantiated and generated. An experimental methodology incorporating a solution database and appropriate metrics is offered as a proposed evaluation framework allowing absolute comparisons of specific MOEA approaches. Taken together, this document's classifications, analyses, and new innovations present a complete, contemporary view of current MOEA "state of the art" and possible future research. Researchers with basic EA knowledge may also use part of it as a largely self-contained introduction to MOEAs.

Multiobjective evolutionary algorithms: classifications, analyses, and new innovations

From the Publisher:
This work covers all aspects of physical design. The book is a core reference for graduate students and CAD professionals. For students, concept and algorithms are presented in an intuitive manner. For CAD professionals, the material presents a balance of theory and practice. An extensive bibliography is provided which is useful for finding advanced material on a topic. At the end of each chapter, exercises are provided, which range in complexity from simple to research level.

Algorithms for VLSI Physical Design Automation

This paper is the result of a literature study carried out by the authors. It is a review of the different attempts made to solve the Travelling Salesman Problem with Genetic Algorithms. We present crossover and mutation operators, developed to tackle the Travelling Salesman Problem with Genetic Algorithms with different representations such as: binary representation, path representation, adjacency representation, ordinal representation and matrix representation. Likewise, we show the experimental results obtained with different standard examples using combination of crossover and mutation operators in relation with path representation.

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Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators

Hybrid metaheuristics have received considerable interest these recent years in the field of combinatorial optimization. A wide variety of hybrid approaches have been proposed in the literature. In this paper, a taxonomy of hybrid metaheuristics is presented in an attempt to provide a common terminology and classification mechanisms. The taxonomy, while presented in terms of metaheuristics, is also applicable to most types of heuristics and exact optimization algorithms.

As an illustration of the usefulness of the taxonomy an annoted bibliography is given which classifies a large number of hybrid approaches according to the taxonomy.

A Taxonomy of Hybrid Metaheuristics

We consider a fully SAT-based method of unbounded symbolic model checking based on computing Craig interpolants. In benchmark studies using a set of large industrial circuit verification instances, this method is greatly more efficient than BDD-based symbolic model checking, and compares favorably to some recent SAT-based model checking methods on positive instances.

Interpolation and SAT-based model checking

The use of parallel architectures for the solution of CPU and memory critical problems in the electronic CAD area has been limited up to now by several factors, like the lack of efficient algorithms the reduced portability of the code, and the cost of the hardware. However, portable message-passing libraries are now available, and the same code runs on high-cost supercomputers, as well as on common workstation networks. The paper presents an effective ATPG system for large sequential circuits developed using the PVM library and based on a genetic algorithm. The tool, named GATTO has been run on a DEC Alpha AXP farm and on a CM-5. Experimental results are provided.

A portable ATPG tool for parallel and distributed systems

Testing is a key issue in the design and production of digital circuits and the adoption of built-in self test techniques is increasingly popular. This paper shows an application in the field of electronic CAD of the Selfish Gene algorithm, an evolutionary algorithm based on a recent interpretation of the Darwinian theory. A three-phase optimization algorithm is exploited for determining the structure of a built-in self test architecture that is able to achieve good fault coverage results with a reduced area overhead. Experimental results show that the attained fault coverage is substantially higher than what can be obtained by previously proposed methods with comparable area requirements.

Exploiting the Selfish Gene algorithm for evolving hardware cellular automata

In the past performance counters have been available to top-end microprocessors as hardware luxuries for profiling critical applications. Today, on the contrary', several desktop microprocessors contain hardware support for monitoring performance events. This paper proposes a new approach to automatic test program generation that exploits such hardware to monitor specific micro-architectural events. In the approach, the generation tool repeatedly evaluates and improves candidate programs directly running on the target microprocessor: candidate programs are not "simulated", but rather "executed". The fast evaluation of candidate tests enables the use of an automatic methodology even on large designs. As a case study, an experiment targeting the Intel/spl reg/ Pentium/spl reg/ 4 microprocessor is reported.

Automatic test programs generation driven by internal performance counters

Many safety-critical applications today rely on computer-based systems in which several computing nodes communicate through a network backbone. As the complexity of the systems under analysis grows, designers must devise fault-injection models that strike a balance between two conflicting requirements: On the one hand, models should be as close as possible to a system's physical implementation to reflect precisely the effects of real faults. On the other hand, abstract, easily manageable models minimize the time required for the fault-injection experiments, letting designers analyze sets of faults wide enough to provide statistically meaningful information. In addressing this issue, we have devised a fault-injection environment to study the effects of soft errors in CAN networks. Our cosimulation environment consists of two modules. The first, a traffic generator module implemented in software, emulates the applications running in each node of the network. The second, a network backbone module implemented in hardware, simulates the activities involved in information exchange between network nodes, in compliance with the CAN protocol specification. To allow evaluation of complex workloads as well as large fault lists, we use an FPGA board to emulate the network backbone module. This enables cycle-accurate simulations of the entire network's behavior with very low speed penalties.

Early, accurate dependability analysis of CAN-based networked systems

This paper describes GGOAL, a genetic algorithm for the minimization of glass loss in cutting large sheets into several pieces. The algorithm takes into account several industrial constraints, stemming both from the glass cutting technology and from the requirement that the optimization must run in real-time, concurrently with the cutting operation. The algorithm delivers comparable or better results than optimization procedures embedded in comprehensive commercial software systems, and is now distributed with all flat glass cutting machines sold by Bottero SpA.

Matteo Sonza Reorda

Papers

A portable ATPG tool for parallel and distributed systems

Exploiting the Selfish Gene algorithm for evolving hardware cellular automata

Automatic test programs generation driven by internal performance counters

Early, accurate dependability analysis of CAN-based networked systems

Optimizing area loss in flat glass cutting