Adaption of evolutionary programming to the prediction of solar flares

Adapting evolutionary programming to prediction of solar flare

Evolutionary Computing for Operating Point Analysis of Nonlinear Circuits

The DC operating point of an electronic circuit is conventionally found using the Newton-Raphson method. This method is not globally convergent and can only find one solution of the circuit at a time. In this paper, evolutionary computing methods, including Genetic Algorithms, Evolutionary Programming, Evolutionary Strategies and Differential Evolution are explored as possible alternatives to Newton-Raphson. These techniques have been implemented in a trial simulator. Results are presented showing that Evolutionary Computing methods are globally convergent and can find multiple solutions to circuits. The CPU time for these new methods is poor compared with Newton-Raphson, but better implementations and the use of hybrid methods suggest that further work in this area would prove fruitful

The role of concurrency in an evolutionary view of programming abstractions

In this paper we examine how concurrency has been embodied in mainstream programming languages. In particular, we rely on the evolutionary talking borrowed from biology to discuss major historical landmarks and crucial concepts that shaped the development of programming languages. We examine the general development process, occasionally deepening into some language, trying to uncover evolutionary lineages related to specific programming traits. We mainly focus on concurrency, discussing the different abstraction levels involved in present-day concurrent programming and emphasizing the fact that they correspond to different levels of explanation. We then comment on the role of theoretical research on the quest for suitable programming abstractions, recalling the importance of changing the working framework and the way of looking every so often. This paper is not meant to be a survey of modern mainstream programming languages: it would be very incomplete in that sense. It aims instead at pointing out a number of remarks and connect them under an evolutionary perspective, in order to grasp a unifying, but not simplistic, view of the programming languages development process

A note on evolutionary stochastic portfolio optimization and probabilistic constraints

In this note, we extend an evolutionary stochastic portfolio optimization framework to include probabilistic constraints. Both the stochastic programming-based modeling environment as well as the evolutionary optimization environment are ideally suited for an integration of various types of probabilistic constraints. We show an approach on how to integrate these constraints. Numerical results using recent financial data substantiate the applicability of the presented approach

Evolution of a robotic soccer player

Robotic soccer is a complex domain where, rather than hand-coding computer programs to control the players, it is possible to create them through evolutionary methods. This has been successfully done before by using genetic programming with high-level genes. Such an approach is, however, limiting. This work attempts to reduce that limit by evolving control programs using genetic programming with low-level nodes

Linear Programming by Solving Systems of Differential Equations Using Game Theory

In this paper we will solve some linear programming problems by solving systems of differential equations using game theory. The linear programming problem must be a classical constraints problem or a classical menu problem, i.e. a maximization/minimization problem in the canonical form with all the coefficients (from objective function, constraints matrix and right sides) positive. Firstly we will transform the linear programming problem such that the new problem and its dual have to be solved in order to find the Nash equilibrium of a matriceal game. Next we find the Nash equilibrium by solving a system of differential equations as we know from evolutionary game theory, and we express the solution of the obtained linear programming problem (by the above transformation of the initial problem) using the Nash equilibrium and the corresponding mixed optimal strategies. Finally, we transform the solution of the obtained problem to obtain the solution of the initial problem. We make also a program to implement the algorithm presented in the paper.Linear programming, evolutionary game theory, Nash equilibrium.

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system