Evolutionary computation is becoming common in the solution of difficult, realworld problems in industry, medicine, and defense. This paper reviews some of the practical advantages to using evolutionary algorithms as compared with classic methods of optimization or artificial intelligence. Specific advantages include the flexibility of the procedures, as well as the ability to self-adapt the search for optimum solutions on the fly. As desktop computers increase in speed, the application of evolutionary algorithms will become routine. 1 Introduction Darwinian evolution is intrinsically a robust search and optimization mechanism. Evolved biota demonstrate optimized complex behavior at every level: the cell, the organ, the individual, and the population. The problems that biological species have solved are typified by chaos, chance, temporality, and nonlinear interactivities. These are also characteristics of problems that have proved to be especially intractable to classic methods of o...

Evolutionary programming is a method for simulating evolution that has been investigated for almost 40 years. When originally introduced, the available computing equipment was quite slow and difficult to use as measured by current standards. This paper provides a series of experiments that follow the framework of the original approach from the early 1960s, brought up to date with current computing machinery. A brief review of evolutionary programming and its relationship to other methods of evolutionary computation, specifically genetic algorithms and evolution strategies, is also offered. Keywords: evolutionary programming, evolutionary computation, forecasting, control. 1. INTRODUCTION There are three main lines of investigation within the current framework of evolutionary computation: (1) genetic algorithms, (2) evolution strategies, and (3) evolutionary programming. Reviews of these methods are offered in several recent books 1-5 . Each of these methods has developed over more ...

Our previous work focused on the synthesis of sequential circuits based on a partial input/output sequence. As the behavioural description of the target circuit is not known the correctness of the result can not be verified. This paper proposes a method which increases the correctness percentage of the finite-state machine (FSM) synthesis using multiple partial input/output sequences. The synthesizer is based on Genetic Algorithm. The experimental results show that the correctness percentage can be increased to 100% by increasing of the number of input/output sequences.

Although the interest in nature-inspired optimization of dynamic problems has been growing constantly over the past decade, very little has been done to analyze and characterize a changing fitness landscape. However, it would be very helpful for algorithm development to have a better understanding of the nature of fitness changes in dynamic real-world problems. In this paper, we propose a number of measures that can be used to analyze and characterize the dynamism in a problem changing over time. Additionally, we introduce a new dynamic multi-dimensional knapsack problem as a close-to-real-world test problem.

Our previous work focused on the synthesis of sequential circuits based on a partial input/output sequence. As the behavioural description of the target circuit is not known the correctness of the result can not be verified. This paper proposes a method which increases the correctness percentage of the finite-state machine (FSM) synthesis using multiple partial input/output sequences. The synthesizer is based on Genetic Algorithm. The experimental results show that the correctness percentage can be increased to 100% by increasing of the number of input/output sequences. 1. Introduction A finite-state machine (FSM) can be constructed from the understanding of its behavior. Each state must be identified to define the state transition function and the output function. Given a behavioral description, the target FSM can be synthesized by many conventional methods. In contrast, this paper proposes a different approach: an FSM is synthesized not from a behavioural description but from part...