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

Cartesian Genetic Programming in Evolutionary Art

Import 22/07/2015Táto práca sa zaoberá evolučným umením so zameraním na využitie karteziánskeho genetického programovania. Cieľom práce je zhodnotiť využitie karteziánskeho genetického programovania v evolučnom umení a jeho porovnanie s ostatnými evolučnými technikami. V práci sme sa zamerali na problémy evolučného umenia, analýzu rôznych evolučných algoritmov a možnosti reprezentácie generovaného umenia formou obrázkov. Zhodnotíme výhody a nevýhody evolučného umenia a jednotlivých evolučných algoritmov v porovnaní s karteziánskym genetickým programovaním. Pre overenie a testovanie implementujeme aplikáciu generujúcu evolučné umenie na základe interaktívnych evolučných výpočtov formou obrázkov.This thesis deals with evolutionary art with focus on the use of cartesian genetic programming. The aim of this work is to evaluate the use of cartesian genetic programming in evolutionary art and its comparison with other evolutionary techniques. In this thesis we focused on problems of evolutionary art, analysis of different evolutionary algorithms and possibilities of representation of generated art in form of images. We evaluate advantages and disadvantages of evolutionary art and different forms of evolutionary algorithms compared to cartesian genetic programming. For verification and testing purposes we implement application generating evolutionary art based on interactive evolutionary computations in the form of images.460 - Katedra informatikyvýborn

Incorporating characteristics of human creativity into an evolutionary art algorithm (journal article)

A perceived limitation of evolutionary art and design algorithms is that they rely on human intervention; the artist selects the most aesthetically pleasing variants of one generation to produce the next. This paper discusses how computer generated art and design can become more creatively human-like with respect to both process and outcome. As an example of a step in this direction, we present an algorithm that overcomes the above limitation by employing an automatic fitness function. The goal is to evolve abstract portraits of Darwin, using our 2nd generation fitness function which rewards genomes that not just produce a likeness of Darwin but exhibit certain strategies characteristic of human artists. We note that in human creativity, change is less choosing amongst randomly generated variants and more capitalizing on the associative structure of a conceptual network to hone in on a vision. We discuss how to achieve this fluidity algorithmically

Incorporating characteristics of human creativity into an evolutionary art algorithm

A perceived limitation of evolutionary art and design algorithms is that they rely on human intervention; the artist selects the most aesthetically pleasing variants of one generation to produce the next. This paper discusses how computer generated art and design can become more creatively human-like with respect to both process and outcome. As an example of a step in this direction, we present an algorithm that overcomes the above limitation by employing an automatic fitness function. The goal is to evolve abstract portraits of Darwin, using our 2nd generation fitness function which rewards genomes that not just produce a likeness of Darwin but exhibit certain strategies characteristic of human artists. We note that in human creativity, change is less choosing amongst randomly generated variants and more capitalizing on the associative structure of a conceptual network to hone in on a vision. We discuss how to achieve this fluidity algorithmically

Introduction to Evolutionary Algorithms

Real-world has many optimization scenarios with multiple constraints and objective functions that are discontinuous, nonlinear, non-convex, and multi-modal in nature. Also, the optimization problems are multi-dimensional with mixed types of variables like integer, real, discrete, binary, and having a different range of values which demands normalization. Hence, the search space of the problem cannot be smooth. Evolutionary algorithms have started gaining attention and have been employed for computational processes to solve complex engineering problems. Because it has become an instrument for research scientists and engineers who need to apply the supremacy of the theory of evolution to shape any optimization-based research problems and articles. In this chapter, there is a comprehensive introduction to the optimization field with the state-of-the-art in evolutionary computation. Though many books have described such areas of optimization in any form as evolution strategies, genetic programming, genetic algorithms, and evolutionary programming, evolutionary algorithms, that is, evolutionary computation is remarkable for considering it to discuss in detail as a general class