Evolutionary algorithms (EAs) are often wellsuited for optimization problems involving several, often conflicting objectives. Since 1985, various evolutionary approaches to multiobjective optimization have been developed that are capable of searching for multiple solutions concurrently in a single run. However, the few comparative studies of different methods presented up to now remain mostly qualitative and are often restricted to a few approaches. In this paper, four multiobjective EAs are compared quantitatively where an extended 0/1 knapsack problem is taken as a basis. Furthermore, we introduce a new evolutionary approach to multicriteria optimization, the Strength Pareto EA (SPEA), that combines several features of previous multiobjective EAs in a unique manner. It is characterized by (a) storing nondominated solutions externally in a second, continuously updated population, (b) evaluating an individual's fitness dependent on the number of external nondominated points that domina...

The goal of getting computers to automatically solve problems is central to artificial intelligence, machine learning, and the broad area encompassed by what Turing called “machine intelligence ” [161, 162].

Abstract. In this paper, we consider system-level synthesis as the problem of optimally mapping a task-level specification onto a heterogeneous hardware/software architecture. This problem requires (1) the selection of the architecture (allocation) including general purpose and dedicated processors, ASICs, busses and memories, (2) the mapping of the specification onto the selected architecture in space (binding) and time (scheduling), and (3) the design space exploration with the goal to find a set of implementations that satisfy a number of constraints on cost and performance. Existing methodologies often consider a fixed architecture, perform the binding only, do not reflect the tight interdependency between binding and scheduling, do not consider communication (tasks and resources), or require long run-times preventing design space exploration, or yield only one implementation with optimal cost. Here, a model is introduced that handles all mentioned requirements and allows the task of system-synthesis to be specified as an optimization problem. The application and adaptation of an Evolutionary Algorithm to solve the tasks of optimization and design space exploration is described. Keywords: System-synthesis, hardware/software partitioning, design space exploration, evolutionary algorithms. 1.

The problem of evolving an artificial ant to follow the Santa Fe trail is used to study the well known genetic programming feature of growth in solution length. Known variously as "bloat", "fluff" and increasing "structural complexity", this is often described in terms of increasing "redundancy" in the code caused by "introns". Comparison between runs with and without fitness selection pressure, backed by Price's Theorem, shows the tendency for solutions to grow in size is caused by fitness based selection. We argue that such growth is inherent in using a fixed evaluation function with a discrete but variable length representation. With simple static evaluation search converges to mainly finding trial solutions with the same fitness as existing trial solutions. In general variable length allows many more long representations of a given solution than short ones. Thus in search (without a length bias) we expect longer representations to occur more often and so representation length to te...

Evolutionary Algorithms are a common probabilistic optimization method based on the model of natural evolution. One important operator in these algorithms is the selection scheme for which in this paper a new description model based on fitness distributions is introduced.

Abstract. The problem of evolving, using mutation, an artificial ant to follow the Santa Fe trail is used to study the well known genetic programming feature of growth in solution length. Known variously as “bloat”, “fluff ” and increasing “structural complexity”, this is often described in terms of increasing “redundancy ” in the code caused by “introns”. Comparison between runs with and without fitness selection pressure, backed by Price’s Theorem, shows the tendency for solutions to grow in size is caused by fitness based selection. We argue that such growth is inherent in using a fixed evaluation function with a discrete but variable length representation. With simple static evaluation search converges to mainly finding trial solutions with the same fitness as existing trial solutions. In general variable length allows many more long representations of a given solution than short ones. Thus in search (without a length bias) we expect longer representations to occur more often and so representation length to tend to increase. I.e. fitness based selection leads to bloat.

Evolutionary computation (EC) consists of the design and analysis of probabilistic algorithms inspired by the principles of natural selection and variation. Genetic Programming (GP) is one subfield of EC that emphasizes desirable features such as the use of procedural representations, the capability to discover and exploit intrinsic characteristics of the application domain, and the flexibility to adapt the shape and complexity of learned models. Approaches that learn monolithic representations are considerably less likely to be effective for complex problems, and standard GP is no exception. The main goal of this dissertation is to extend GP capabilities with automatic mechanisms to cope with problems of increasing complexity. Humans succeed here by skillfully using hierarchical decomposition and abstraction mechanisms. The translation of such mechanisms into a general computer implementation is a tremendous challenge, which requires a firm understanding of the interplay between repr...

An evolutionary algorithm is used as an engine for discovering new designs of digital circuits, particularly arithmetic functions. These designs are often radically different from those produced by top-down, human, rule-based approaches. It is argued that by studying evolved designs of gradually increasing scale, one might be able to discern new, efficient, and generalisable principles of design. The ripplecarry adder principle is one such principle that can be inferred from evolved designs for one and two-bit adders. Novel evolved designs for three-bit binary multipliers are given that are 20% more efficient (in terms of number of two-input gates used) than the most efficient known conventional design. 1 Introduction Traditionally physical systems (e.g. bridges, computers, mobile phones) have been designed by engineers using complex collections of rules and principles. The design process is top-down in nature and begins with a precise specification. This contrasts very stron...

Genetic programming trees have a strong tendency to grow rapidly and relatively independent of fitness, a serious flaw which has received considerable attention in the genetic programming literature. Much of this literature has implicated introns, subtree structures with no effect on the an individual's fitness assessment. The propagation of inviable code, a certain kind of intron, has been especially linked to tree growth. However this paper presents evidence which shows that denying inviable code the opportunity to propagate actually increases tree growth. The paper argues that rather than causing tree growth, a rise in inviable code is in fact an expected result of tree growth. Lastly, this paper proposes a more general theory of growth for which introns are merely a symptom. 1 INTRODUCTION An unforseen result of genetic programming's tree-based chromosome is bloat, the uncontrolled growth in the size of individuals over the course of a run. This phenomenon has bee...

Fitness distributions (landscapes) of programs tend to a limit as they get bigger. Markov chain convergence theorems give general upper bounds on the linear program sizes needed for convergence. Tight bounds (exponential in N, N log N and smaller) are given for five computer models (any, average, cyclic, bit flip and Boolean). Mutation randomizes a genetic algorithm population in 1 4 (l + 1)(log(l) + 4) generations. Results for a genetic programming (GP) like model are confirmed by experiment. 1