Applications of evolutionary computation techniques to analog, mixed-signal and RF circuit design - an overview <s> II. BASIC EVOLUTIONARY COMPUTATION <s> A brief introduction is given to the actual mechanics of simulated annealing, and a simple example from an IC layout is used to illustrate how these ideas can be applied. The complexities and tradeoffs involved in attacking a realistically complex design problem are illustrated by dissecting two very different annealing algorithms for VLSI chip floorplanning. Several current research problems aimed at determining more precisely how and why annealing algorithms work are examined. Some philosophical issues raised by the introduction of annealing are discussed. > <s> BIB001 </s> Applications of evolutionary computation techniques to analog, mixed-signal and RF circuit design - an overview <s> II. BASIC EVOLUTIONARY COMPUTATION <s> A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described. <s> BIB002 </s> Applications of evolutionary computation techniques to analog, mixed-signal and RF circuit design - an overview <s> II. BASIC EVOLUTIONARY COMPUTATION <s> This article presents an overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies' foraging behavior, and introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic biological findings on real ants are reviewed and their artificial counterparts as well as the ACO metaheuristic are defined. In the second part of the article a number of applications of ACO algorithms to combinatorial optimization and routing in communications networks are described. We conclude with a discussion of related work and of some of the most important aspects of the ACO metaheuristic. <s> BIB003 </s> Applications of evolutionary computation techniques to analog, mixed-signal and RF circuit design - an overview <s> II. BASIC EVOLUTIONARY COMPUTATION <s> From the Publisher: ::: In the past three decades local search has grown from a simple heuristic idea into a mature field of research in combinatorial optimization. Local search is still the method of choice for NP-hard problems as it provides a robust approach for obtaining high-quality solutions to problems of a realistic size in a reasonable time. This area of discrete mathematics is of great practical use and is attracting ever increasing attention. The contributions to this book cover local search and its variants from both a theoretical and practical point of view, each with a chapter written by leading authorities on that particular aspect. This book is an important reference volume and an invaluable source of inspiration for advanced students and researchers in discrete mathematics, computer science, operations research, industrial engineering and management science. <s> BIB004 </s> Applications of evolutionary computation techniques to analog, mixed-signal and RF circuit design - an overview <s> II. BASIC EVOLUTIONARY COMPUTATION <s> An optimization tool for radio frequency integrated circuits (RFICs) based on an elitist nondominated sorting genetic algorithm is introduced. It casts RF circuit synthesis as a multi-objective optimization problem and produces multiple solutions along the Pareto optimal front. Optimization is followed by sensitivity assessment wherein Monte Carlo simulations are performed for the Pareto points with respect to process, voltage, and temperature variations. The tool is validated in the synthesis of a 5.2-GHz direct-conversion receiver front-end that includes a common-gate differential low-noise amplifier, I/Q down-conversion mixers, and a quadrature voltage-controlled oscillator in a 250-nm SiGe BiCMOS process. <s> BIB005 </s> Applications of evolutionary computation techniques to analog, mixed-signal and RF circuit design - an overview <s> II. BASIC EVOLUTIONARY COMPUTATION <s> This paper presents a 3rd-order continuous-time Delta-Sigma modulator with a resolution of 10 bits for a 10 MHz signal bandwidth. It is designed in a standard 0.18 μm CMOS technology and consumes only 6 mW. After the design/selection of the topologies for the integrators, comparator and D/A converters, optimal sizing of the complete modulator was ensured by using a hierarchical bottom-up, multi-objective evolutionary design methodology. With this methodology, a set of Pareto-optimal modulator designs is generated by using Pareto-optimal performance solutions of the hierarchically decomposed lower-level subblocks. From the generated Pareto-optimal design set, a final optimal design is chosen that complies with the specifications for the 802.11a/b/g WLAN standard and has minimal power consumption. <s> BIB006 </s> Applications of evolutionary computation techniques to analog, mixed-signal and RF circuit design - an overview <s> II. BASIC EVOLUTIONARY COMPUTATION <s> We present a novel heuristic for optimizing analog circuit performances. It deals with generating the Pareto front using the topological properties of the feasible solution space. This heuristic allows us generating optimal values of circuit parameters in reduced computation time and memory consumption. Unlike basic metaheuristics, it does not need optimization background from the user in order to be easily adapted to different applications. It can thus be smoothly integrated into an automated design flow. This novel approach enables us to further improve (good) performances that were already reached using other optimizing techniques. Robustness of the algorithm was proved using specific difficult test problems. <s> BIB007
TECHNIQUES Evolutionary Computation (EC) is a very active research area of computer science  . An important class of EC algorithms is Evolutionary Algorithms (EA), which are inspired from metaphors of biological processes. EAs encompass numerous approaches currently used in optimization, such as genetic algorithms, evolution strategies, etc. In short, an EA is a stochastic iterative procedure for generating tentative solutions for an optimization problem. The algorithm handles a set of individuals (candidate solutions), whose fitness (quality) determines the probability that they will be kept to be used as seeds for generating other individuals  . EAs have been successfully applied to solve circuit design problems BIB005 , BIB007 - BIB006 . Despite this fact, the continuous evolution of technology imposes hard constraints on such algorithms which lead to many real difficulties, such as prohibitive evaluation cost (related with the size, complexity and fidelity of the circuit model), a large number of function evaluations, a large number of parameters to handle, etc. Another class of nature-inspired EC algorithms, recently proposed, is swarm intelligence algorithms. They focus on animal behavior and insect conduct in order to develop some metaheuristics which can mimic their problem-solution abilities, namely Ant Colony (AC) BIB003 , Artificial Bee Colony (ABC)  , Wasp Nets  and Particle Swarm Optimization (PSO) BIB002 . The ability of these algorithms to solve AMS/RF design problems is also being investigated. It is to be mentioned that some heuristic-based mathematical approaches have also been used to deal with AMS/RF optimization problems, such as local search BIB004 , simulated annealing BIB001 , and scatter search  . Traditionally, global optimization algorithms have been single-objective, i.e., they try to minimize/maximize a single objective function. More recently, algorithms specifically devoted to solve multi-objective optimization problems have become object of intensive research. Actually, solving real optimization problems, i.e., obtaining 'good' solutions in reasonable computation time while efficiently allocating computing resources, still presents numerous open challenges.