Chapter 8 - Application of Evolutionary Computation to Protein Folding with Specialized Operators

March 1-3, 1995, San Diego, California Evolutionary programming is one of the predominate algorithms withing the rapidly expanding field of evolutionary computation. These edited contributions to the Fourth Annual Conference on Evolutionary Programming are by leading scientists from academia, industry, and defense. The papers describe both the theory and practical application of evolutionary programming, as well as other methods of evolutionary computation including evolution strategies, genetic algorithms, genetic programming, and cultural algorithms.Topics include :- Novel Areas of Evolutionary Programming and Evolution Strategies.- Evolutionary Computation with Medical Applications.- Issues in Evolutionary Optimization Pattern Discovery, Pattern Recognition, and System Identification.- Hierarchical Levels of Learning.- Self-Adaptation in Evolutionary Computation.- Morphogenic Evolutionary Computation.- Issues in Evolutionary Optimization.- Evolutionary Applications to VLSI and Part Placement.- Applications of Evolutionary Computation to Biology and Biochemistry Control.- Applications of Evolutionary Computation.- Genetic and Inductive Logic Programming.- Genetic Neural Networks.- The Future of Evolutionary Computation.A Bradford Book. Complex Adaptive Systems series

March 1-3, 1995, San Diego, California Evolutionary programming is one of the predominate algorithms withing the rapidly expanding field of evolutionary computation. These edited contributions to the Fourth Annual Conference on Evolutionary Programming are by leading scientists from academia, industry, and defense. The papers describe both the theory and practical application of evolutionary programming, as well as other methods of evolutionary computation including evolution strategies, genetic algorithms, genetic programming, and cultural algorithms.Topics include :- Novel Areas of Evolutionary Programming and Evolution Strategies.- Evolutionary Computation with Medical Applications.- Issues in Evolutionary Optimization Pattern Discovery, Pattern Recognition, and System Identification.- Hierarchical Levels of Learning.- Self-Adaptation in Evolutionary Computation.- Morphogenic Evolutionary Computation.- Issues in Evolutionary Optimization.- Evolutionary Applications to VLSI and Part Placement.- Applications of Evolutionary Computation to Biology and Biochemistry Control.- Applications of Evolutionary Computation.- Genetic and Inductive Logic Programming.- Genetic Neural Networks.- The Future of Evolutionary Computation.A Bradford Book. Complex Adaptive Systems series

Molecular biologists are currently faced with an array of computationally complex optimization problems. Over the past six years, evolutionary computation has been demonstrated to be useful for some of these problems, in particular RNA and protein structure prediction. This survey will focus on applications of evolutionary programming and genetic algorithms. Future applications of evolutionary computation in the medical and molecular sciences are suggested. The problems faced in computer-aided molecular design represent a challenging new testing ground for algorithms incorporating the evolutionary process.KeywordsGenetic AlgorithmFitness FunctionEvolutionary ComputationProtein Structure PredictionLower Energy StructureThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This chapter examines various applications of evolutionary computation (EC)-based meta-models to augment or replace the conventional use of numerical simulation and optimisation within the context of hydroinformatics. Evolutionary computation-based optimisation techniques are increasingly used in a wide range of water and environmental applications either as optimisation, analysis or design tools. However, despite the advances in computer power, it may still be impractical to rely exclusively on computationally expensive (time-consuming) simulation for many real-world complex problems. The meta-model investigated in this chapter can take various forms and, when coupled with a genetic algorithm, forms a fast and effective hybridisation. Three examples, including calibration of a rainfall–runoff model, modified Monte Carlo sampling of a kinematic wave model and the design and robust rehabilitation of water distribution models, are then used to illustrate the concept of EC-based meta-models. The proposed meta-model reduces the number of simulation runs required in the numerical model considerably, thus making the optimisation and statistical analysis of computationally intensive simulation models viable.

The application of genetic and evolutionary computation to problems in medicine has increased rapidly over the past five years, but there are specific issues and challenges that distinguish it from other real-world applications. Obtaining reliable and coherent patient data, establishing the clinical need and demonstrating value in the results obtained are all aspects that require careful and detailed consideration. This tutorial is based on research which uses genetic programming (a representation of Cartesian Genetic Programming) in the diagnosis and monitoring of Parkinson's disease, Alzheimer's disease and other neurodegenerative conditions, as well as in the early detection of breast cancer through automated assessment of mammograms. The work is supported by multiple clinical studies in progress in the UK (Leeds General Infirmary), USA (UCSF), UAE (Dubai Rashid Hospital), Australia (Monash Medical Center) and Singapore (National Neuroscience Institute). The technology is protected through three patent applications and a University spin-out company marketing four medical devices. The tutorial considers the following topics: Introduction to medical applications of genetic and evolutionary computation and how these differ from other real-world applications Overview of past work in the from a medical and evolutionary computation point of view Three case examples of medical applications: i. diagnosis and monitoring of Parkinson's disease ii. detection of beast cancer from mammograms iii. cancer screening using Raman spectroscopy Practical advice on how to get started working on medical applications, including existing medical databases and conducting new medical studies, commercialization and protecting intellectual property. Summary, further reading and links

Application of evolutionary computation on ensemble forecast of quantitative precipitation

Many GECCO papers discuss lessons learned in a particular application, but few papers discuss lessons learned over an ensemble of problem areas. A scan of the tables of contents of the Proceedings from GECCO 2005 and 2006 showed no paper title stressing lessons learned although the term "pitfall" appeared occasionally in abstracts, typically applying to a particular practice. We present in this paper a set of broadly applicable "lessons learned" in the application of evolutionary computing (EC) techniques to a variety of problem areas and present advice related to encoding, running, monitoring, and managing an evolutionary computing task.

Engine mount tuning is a multi‐disciplinary exercise since it affects Idle‐shake, Road‐shake and power‐train noise response. Engine inertia is often used as a tuned absorber for controlling suspension resonance related road‐shake issues. Last but not least, vehicle ride and handling may also be affected by mount tuning. In this work, Torque‐Roll‐Axis (TRA) decoupling of the rigid powertrain was used as a starting point for mount tuning. Nodal point of flexible powertrain bending was used to define the envelop for transmission mount locations. The frequency corresponding to the decoupled roll mode of the rigid powertrain was then adjusted for idle‐shake and road‐shake response management.A TRA decoupling procedure, cast as a multi‐objective optimization problem, was applied to a body‐on‐frame sport‐utility vehicle powertrain system. In addition to a standard gradient based optimization algorithm, available in commercial finite element software, an evolutionary computation paradigm known as Evolutionary Strategies (ES) was used to solve the optimization problem. The primary advantages of evolutionary computation over gradient based algorithms are as follows: i) They are less likely to get trapped in local minima and less dependent on initial values of the design parameters and therefore able to handle multi‐modal optimization problems unlike gradient based algorithms, ii) They produce a population of viable solutions, unlike gradient based algorithms which yields a single solution. The second advantage is very attractive in a production environment since packaging and other multi‐disciplinary constraints often require multiple quality solutions for the same problem. The process outlined in this work was verified by exercising a full‐vehicle finite element model. The process produced a set of production feasible powertrain mount parameters for acceptable idle and road shake performance.

Traffic congestion has become one of the major problems of smart travel. The application of evolutionary computation (EC) for traffic signal timing optimization (TSTO) can effectively alleviate traffic congestion at a single intersection. However, while in more complicated regional traffic signal timing optimization (RTSTO) problems, the canonical EC algorithm such as particle swarm optimization (PSO) still has limitation due to population prematurity. In this paper, a multi-exemplar learning (MEL) strategy is adopted to improve the diversity of the population, so that the particles can have more opportunities to explore the search space. Furthermore, multiple traffic indicators are used in this paper to measure the comprehensive performance of the solution. Moreover, the microsimulation software is adopted to evaluate the solution to simulate the real-world intersection, making the obtained solution more practical in real-world application. Experiments are conducted to investigate the effectiveness and efficiency of MEL-PSO. The results show that the MEL-PSO algorithm is more effective and efficient than the compared algorithms on RTSTO problems.

One of the main research problems in Structural Bioinformatics is related to the prediction of three-dimensional structures (3-D) of polypeptides or proteins. The rate at which amino acid sequences are identified is increasing faster than the 3-D protein structure determination by experimental methods. Computational prediction methods have been developed during the last years, but the problem still remains challenging because of the complexity and high dimensionality of a protein conformational search space. In this article we present a hybrid genetic algorithm for the Protein Structure Prediction (PSP) Problem. A genetic algorithm is combined with a structured population, and it is hybridized with a path-relinking procedure that helps the algorithm to scape from local minima. We perform a set of experiments and show that the proposed hybrid genetic algorithm is effective in finding good quality solutions for the PSP Problem.

BackgroundProtein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task.ResultsIn order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods.ConclusionsThe hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively.

Location‐only and use‐availability data: analysis methods converge

This paper introduces the basic theory and method of carbon dioxide (CO2) retrieval. The key step is to search for the optimal solution and the random search algorithm Genetic Algorithm (GA) which can effectively avoid the local optimization. We first investigate the basic principles of GA in CO₂ retrieval and then design the corresponding encoding and decoding methods as well as the fitness function. This newly-developed GA is further applied to retrieve the atmospheric CO₂ concentration using Atmospheric Infrared Sounder (AIRS) observations from January 2006 to December 2008 centered at 20°N, 144°E. Compared to the aircraft measurements, the GA retrieval yields the small root mean square error of 1.13 ppmv and reproduces good results with the observed seasonal cycle.

In this study, we used the Hydrophobic-Polar (HP) two-dimensional square and three-dimensional cubic lattice models for the problem of Protein structure prediction (PSP). This kind of lattice reduces computational time and calculations, and the conformational space from 9n to 4n−2, even in this context, it is an appropriate benchmark problem for genetic algorithms. The contributions of the paper consist in: (1) implementation of a high-performing novel Genetic algorithm (GA); instead of considering only the self- avoiding walk (SAW) conformations approached in other work, we decided to allow any conformation to appear in the population at all stages of the proposed all conformations biomimetic Genetic Algorithm (ACGA). This increases the probability of achieving good native conformations (SAW ones), with the lowest energy that has the minimum number of contacts in the lattice, the one with the maximum topological neighbors. In addition to classical crossover and mutation operators, (2) we introduced specific translation operators for these two operations. We have proposed and implemented an HP Protein Visualizer tool which offers interpretability, a hybrid approach in that the visualizer give some insight to the algorithm, that analyse and optimise protein structures HP model. The program resulted based on performed research, provides a molecular modeling tool for studying protein folding using technologies such as React, Next.js, and Three.js for 3D rendering, and includes optimization algorithms to simulate protein folding.

Energy-efficient virtual machine placement in data centres via an accelerated Genetic Algorithm with improved fitness computation