4,175 research outputs found
A Review on the Application of Natural Computing in Environmental Informatics
Natural computing offers new opportunities to understand, model and analyze
the complexity of the physical and human-created environment. This paper
examines the application of natural computing in environmental informatics, by
investigating related work in this research field. Various nature-inspired
techniques are presented, which have been employed to solve different relevant
problems. Advantages and disadvantages of these techniques are discussed,
together with analysis of how natural computing is generally used in
environmental research.Comment: Proc. of EnviroInfo 201
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Artificial Immune Systems - Models, algorithms and applications
Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun
"Going back to our roots": second generation biocomputing
Researchers in the field of biocomputing have, for many years, successfully
"harvested and exploited" the natural world for inspiration in developing
systems that are robust, adaptable and capable of generating novel and even
"creative" solutions to human-defined problems. However, in this position paper
we argue that the time has now come for a reassessment of how we exploit
biology to generate new computational systems. Previous solutions (the "first
generation" of biocomputing techniques), whilst reasonably effective, are crude
analogues of actual biological systems. We believe that a new, inherently
inter-disciplinary approach is needed for the development of the emerging
"second generation" of bio-inspired methods. This new modus operandi will
require much closer interaction between the engineering and life sciences
communities, as well as a bidirectional flow of concepts, applications and
expertise. We support our argument by examining, in this new light, three
existing areas of biocomputing (genetic programming, artificial immune systems
and evolvable hardware), as well as an emerging area (natural genetic
engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin
Leo: Lagrange Elementary Optimization
Global optimization problems are frequently solved using the practical and
efficient method of evolutionary sophistication. But as the original problem
becomes more complex, so does its efficacy and expandability. Thus, the purpose
of this research is to introduce the Lagrange Elementary Optimization (Leo) as
an evolutionary method, which is self-adaptive inspired by the remarkable
accuracy of vaccinations using the albumin quotient of human blood. They
develop intelligent agents using their fitness function value after gene
crossing. These genes direct the search agents during both exploration and
exploitation. The main objective of the Leo algorithm is presented in this
paper along with the inspiration and motivation for the concept. To demonstrate
its precision, the proposed algorithm is validated against a variety of test
functions, including 19 traditional benchmark functions and the CECC06 2019
test functions. The results of Leo for 19 classic benchmark test functions are
evaluated against DA, PSO, and GA separately, and then two other recent
algorithms such as FDO and LPB are also included in the evaluation. In
addition, the Leo is tested by ten functions on CECC06 2019 with DA, WOA, SSA,
FDO, LPB, and FOX algorithms distinctly. The cumulative outcomes demonstrate
Leo's capacity to increase the starting population and move toward the global
optimum. Different standard measurements are used to verify and prove the
stability of Leo in both the exploration and exploitation phases. Moreover,
Statistical analysis supports the findings results of the proposed research.
Finally, novel applications in the real world are introduced to demonstrate the
practicality of Leo.Comment: 28 page
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
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