An automated ligand evolution system using Bayesian optimization algorithm

Ligand docking checks whether a drug chemical called ligand matches the target receptor protein of human organ or not. Docking by computer simulation is becoming popular in drug design process to reduce cost and time of the chemical experiments. This paper presents a novel approach generating optimal ligand structures from scratch based on de novo ligand design approach employing Bayesian optimization algorithm to realize an automated design of drug and other chemical structures. The proposed approach searches an optimal structure of ligand that minimizes bond energy to the receptor protein, and the structure of ligand is generated by adding small fragments of molecules to the base structure. The decision of adding fragments are controlled by Bayesian optimization algorithm which is considered as a promising approach in probabilistic model-building genetic algorithms. We have built a system that automatically generates an optimal structure of ligand, and through numerical experiments performed on a PC cluster, we show the effectiveness of our approach compared to the conventional approach using classical genetic algorithms

Implementation and Optimization of cGA+LS to solve Capacitated VRP over Cell/B.E.

This paper presents a case study to illustrate the design and implementation of cellular Genetic Algorithm (cGA) with Local Search (LS) to solve Capacitated Vehicle Routing Problem (CVRP) over Cell Broadband Engine (Cell BE). Cell BE is a heterogeneous, distributed memory multicore processor architecture for multimedia applications with regular memory access requirements. It has one 64-bit Power Processing Element (PPE) that acts as the main processor and 8 Synergistic Processing Elements (SPEs) with only 256 KB of local memory, each for instructions and data. GAs on the other hand use population based search techniques. Such techniques usually have large memory requirements and show non-uniform memory access patterns. These properties of GAs make their implementation over Cell BE even more challenging. In order to take maximum advantage of the hardware, we propose an asynchronous approach to implement cGA+LS over Cell BE. In this paper, we discuss the implementation and optimization of the proposed method in detail. We compare the proposed method with other state-of-the- art CVRP solvers and synchronous implementation of cGA+LS over Cell BE. We solve existing benchmark problems and achieve considerable speedups. We extend the work further to solve extremely large instances of CVRP compared to ones present in the CVRP literature, and get acceptable results in a reasonable amount of time

Hybrid of genetic algorithm and local search to solve MAX-SAT problem using nVidia CUDA framework

General Purpose computing over Graphical Processing Units (GPGPUs) is a huge shift of paradigm in parallel computing that promises a dramatic increase in performance. But GPGPUs also bring an unprecedented level of complexity in algorithmic design and software development. In this paper we describe the challenges and design choices involved in parallelizing a hybrid of Genetic Algorithm (GA) and Local Search (LS) to solve MAXimum SATisfiability (MAX-SAT) problem on a state-of-the-art nVidia Tesla GPU using nVidia Compute Unified Device Architecture (CUDA). MAX-SAT is a problem of practical importance and is often solved by employing metaheuristics based search methods like GAs and hybrid of GA with LS. Almost all the parallel GAs (pGAs) designed in the last two decades were designed for either clusters or MPPs. Unfortunately, very little research is done on the implementation of such algorithms over commodity graphics hardware. GAs in their simple form are not suitable for implementation over the Single Instruction Multiple Thread (SIMT) architecture of a GPU, and the same is the case with conventional LS algorithms. In this paper we explore different genetic operators that can be used for an efficient implementation of GAs over nVidia GPUs. We also design and introduce new techniques/operators for an efficient implementation of GAs and LS over such architectures. We use nVidia Tesla C1060 to perform several numerical tests and performance measurements and show that in the best case we obtain a speedup of 25×. We also discuss the effects of different optimization techniques on the overall execution time

Language Learning in 3D Virtual World : Using Second Life as a Platform

Eleed : E-Learning and Education, No. 8Second Life (SL) is an ideal platform for language learning. It is called a Multi-User Virtual Environment, where users can have varieties of learning experiences in life-like environments. Numerous attempts have been made to use SL as a platform for language teaching and the possibility of SL as a means to promote conversational interactions has been reported. However, the research so far has largely focused on simply using SL without further augmentations for communication between learners or between teachers and learners in a school-like environment. Conversely, not enough attention has been paid to its controllability which builds on the embedded functions in SL. This study, based on the latest theories of second language acquisition, especially on the Task Based Language Teaching and the Interaction Hypothesis, proposes to design and implement an automatized interactive task space (AITS) where robotic agents work as interlocutors of learners. This paper presents a design that incorporates the SLA theories into SL and the implementation method of the design to construct AITS, fulfilling the controllability of SL. It also presents the result of the evaluation experiment conducted on the constructed AITS