Computational Intelligence for Life Sciences

Computational Intelligence (CI) is a computer science discipline encompassing the theory, design, development and application of biologically and linguistically derived computational paradigms. Traditionally, the main elements of CI are Evolutionary Computation, Swarm Intelligence, Fuzzy Logic, and Neural Networks. CI aims at proposing new algorithms able to solve complex computational problems by taking inspiration from natural phenomena. In an intriguing turn of events, these nature-inspired methods have been widely adopted to investigate a plethora of problems related to nature itself. In this paper we present a variety of CI methods applied to three problems in life sciences, highlighting their effectiveness: we describe how protein folding can be faced by exploiting Genetic Programming, the inference of haplotypes can be tackled using Genetic Algorithms, and the estimation of biochemical kinetic parameters can be performed by means of Swarm Intelligence. We show that CI methods can generate very high quality solutions, providing a sound methodology to solve complex optimization problems in life sciences

Satisfiability Logic Analysis Via Radial Basis Function Neural Network with Artificial Bee Colony Algorithm

Radial Basis Function Neural Network (RBFNN) is a variant of artificial neural network (ANN) paradigm, utilized in a plethora of fields of studies such as engineering, technology and science. 2 Satisfiability (2SAT) programming has been coined as a prominent logical rule that defines the identity of RBFNN. In this research, a swarm-based searching algorithm namely, the Artificial Bee Colony (ABC) will be introduced to facilitate the training of RBFNN. Worth mentioning that ABC is a new population-based metaheuristics algorithm inspired by the intelligent comportment of the honey bee hives. The optimization pattern in ABC was found fruitful in RBFNN since ABC reduces the complexity of the RBFNN in optimizing important parameters. The effectiveness of ABC in RBFNN has been examined in terms of various performance evaluations. Therefore, the simulation has proved that the ABC complied efficiently in tandem with the Radial Basis Neural Network with 2SAT according to various evaluations such as the Root Mean Square Error (RMSE), Sum of Squares Error (SSE), Mean Absolute Percentage Error (MAPE), and CPU Time. Overall, the experimental results have demonstrated the capability of ABC in enhancing the learning phase of RBFNN-2SAT as compared to the Genetic Algorithm (GA), Differential Evolution (DE) algorithm and Particle Swarm Optimization (PSO) algorithm

Stereo Vision 3D Tracking of Multiple Free-Swimming Fish for Low Frame Rate Video

3D multiple fish tracking has gained a significant growing research interest to quantify fish behavior. However, most tracking techniques have used a high frame rate that is currently not viable for real-time tracking applications. This study discusses multiple fish tracking techniques using low frame rate sampling of stereo video clips. The fish are tagged and tracked based on the absolute error of predicted indices using past and present fish centroid locations and a deterministic frame index. In the predictor sub-system, the linear regression and machine learning algorithms intended for nonlinear systems, such as Adaptive Neuro-Fuzzy Inference System (ANFIS), symbolic regression, and Gaussian Process Regression (GPR), were investigated. Results have shown that in the context of tagging and tracking accuracy, the symbolic regression attained the best performance, followed by the GPR, i.e., 74% to 100% and 81% to 91%, respectively. Considering the computation time, symbolic regression resulted in the highest computing lag of approximately 946 ms per iteration, whereas GPR achieved the lowest computing time of 39 ms

An Approach Based on Particle Swarm Optimization for Inspection of Spacecraft Hulls by a Swarm of Miniaturized Robots

The remoteness and hazards that are inherent to the operating environments of space infrastructures promote their need for automated robotic inspection. In particular, micrometeoroid and orbital debris impact and structural fatigue are common sources of damage to spacecraft hulls. Vibration sensing has been used to detect structural damage in spacecraft hulls as well as in structural health monitoring practices in industry by deploying static sensors. In this paper, we propose using a swarm of miniaturized vibration-sensing mobile robots realizing a network of mobile sensors. We present a distributed inspection algorithm based on the bio-inspired particle swarm optimization and evolutionary algorithm niching techniques to deliver the task of enumeration and localization of an a priori unknown number of vibration sources on a simplified 2.5D spacecraft surface. Our algorithm is deployed on a swarm of simulated cm-scale wheeled robots. These are guided in their inspection task by sensing vibrations arising from failure points on the surface which are detected by on-board accelerometers. We study three performance metrics: (1) proximity of the localized sources to the ground truth locations, (2) time to localize each source, and (3) time to finish the inspection task given a 75% inspection coverage threshold. We find that our swarm is able to successfully localize the present so

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Projection-Based Clustering through Self-Organization and Swarm Intelligence

It covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to cluster analysis, the Databionic swarm (DBS). DBS consists of the 3D landscape visualization and clustering of data. The 3D landscape enables 3D printing of high-dimensional data structures. The clustering and number of clusters or an absence of cluster structure are verified by the 3D landscape at a glance. DBS is the first swarm-based technique that shows emergent properties while exploiting concepts of swarm intelligence, self-organization and the Nash equilibrium concept from game theory. It results in the elimination of a global objective function and the setting of parameters. By downloading the R package DBS can be applied to data drawn from diverse research fields and used even by non-professionals in the field of data mining

암석의 기계굴착 성능 예측을 위한 유전자발현프로그래밍과 입자군집최적화에 기초한 혼합형 진화 계산 알고리즘

학위논문(박사) -- 서울대학교대학원 : 공과대학 에너지시스템공학부, 2022. 8. Seokwon Jeon.암반 기계 굴착 기술의 발전으로 기존의 발파 공법이 아닌 기계 굴착을 사용하여 지하 공간을 건설하는 사례가 증가하고 있다. 기계식 암석 굴착 분야에는 다양한 변수 간의 관계에 대한 상당한 수의 결정론적 해법이 있지만, 많은 경우 변수 간의 결정적 관계를 설정하는 것은 극히 어렵다. 그 결과 많은 연구자들이 회귀 분석을 사용하여 이러한 관계를 설명하려고 한다. 암석 파쇄 현상의 복잡하고 비선형적인 특성으로 인해 기존의 함수 피팅 기법에서 요구하는 통계 데이터에 부합하는 비선형 함수의 형태를 합리적으로 결정하기가 쉽지 않다. 따라서 본 연구에서는 기계 굴착 분야의 문제점을 해결하기 위해 유전자 발현 프로그래밍(GEP)과 입자 군집 최적화 (PSO)의 조합을 데이터 분석에 사용하였다. GEP 및 PSO는 진화적 계산 기술이며 GEP-PSO 알고리즘을 통해 데이터 세트에 맞는 비선형 함수의 형식과 상수를 자동으로 찾을 수 있다. 본 연구에서는 임팩트 해머에 대한 성능 예측 모델, 픽커터에 필요한 비에너지 예측 모델, 픽커터에 작용하는 절삭력, 수직력, 횡방향력 예측 모델을 개발하기 위해 알고리즘을 사용하였다. 모든 경우에 GEP-PSO 알고리즘을 사용하여 생성된 결과는 다중 선형 회귀에 의해 생성된 결과와 비교하여 상당히 높은 예측 정확도를 생성함을 확인하였다. 가능한 경우 GEP-PSO 알고리즘에 의해 생성된 결과와 다른 연구자가 개발한 예측 모델을 비교하여 현재 연구 과정에서 개발된 모델의 장점을 보여 줄 것으로 보인다. 높은 수준의 정확도 외에도 GEP-PSO 알고리즘을 사용하여 개발된 모델은 기존 예측 모델의 단점을 상당 부분 극복할 수 있다. 개발된 모델은 얻기 쉬운 입력 매개변수를 거의 요구하지 않으면서 더 많은 신뢰성 및 정확도를 제공하거나 기존 예측 모델에서 무시되었던 중요한 입력 매개변수를 포함하므로 더 유리하다고 볼 수 있다.With the advances in mechanical excavation technology, increasing number of underground spaces are built using mechanical excavation rather than the conventional drilling and blasting method. In the field of mechanical rock excavation, there are a fair number of deterministic solutions for the relations between different variables. However, in many cases, establishing such a relation is extremely difficult. As a result, many researchers try to explain those relations using regression analysis. Due to the complex and non-linear nature of rock cutting phenomenon, it is not easy to reasonably determine the form of the non-linear functions that fit to the statistical data as it is required by the conventional non-linear function fitting techniques. As a result, a combination of Gene Expression Programming (GEP) and Particle Swarm Optimization (PSO) was used for data analysis in this study in order to solve problems in the field of mechanical excavation. GEP and PSO are evolutionary computation techniques and the GEP-PSO algorithm is capable of automatically finding the form and constants of a non-linear function that fits on a data set. The algorithm was used in order to develop a performance prediction model for impact hammer, a prediction model for specific energy required by point attack picks, and models for prediction of cutting, normal, and side force acting on a point attack pick. In all cases, the results generated using the GEP-PSO algorithm produced significantly high prediction accuracy in comparison to those generated by multiple linear regression. When possible, comparisons were made between the results generated by the GEP-PSO algorithm and the prediction models developed by other researchers to show the advantages of the models developed over the course of the present study. In addition to high level of accuracy, the models developed using GEP-PSO algorithm could overcome shortcomings of the existing prediction models to a fair extent. The developed models are more advantageous as they provide more reliability/accuracy while requiring few easy-to-obtain input parameters, and/or they include the significant input parameters that have been neglected by the existing prediction models.1. Introduction 1 2. Literature Review 10 2.1 Impact hammer performance prediction 10 2.1.1 Existing performance prediction models 11 2.1.2 Performance prediction model 13 2.2 Specific energy prediction 14 2.2.1 Parameters with a significant impact on specific energy 16 2.2.2 Specific energy prediction model 22 2.3 Forces acting on a point attack pick 22 2.3.1 Existing force prediction models 23 2.3.2 Parameters with a significant impact on forces 29 2.3.3 Forces prediction models 30 3. Statistical Data 31 3.1 Impact hammer performance 31 3.1.1 Levent-Hisarustu tunnel 31 3.1.2 Uskudar-Cekmekoy tunnel 33 3.2 Specific energy required by point attack picks 37 3.3 Forces applied on point attack picks 41 4. Data Analysis Method 43 4.1 Gene Expression Programming (GEP) 45 4.1.1 Genetic Operators 47 4.1.2 The Basic Flowchart of GEP algorithm 55 4.2 Particle Swarm Optimization (PSO) 56 4.3 GEP-PSO algorithm 58 5. Results and Discussion 64 5.1 The suggested impact hammer performance prediction model 65 5.2 The model suggested for prediction of specific energy required by point attack picks 75 5.3 The suggested models for prediction of forces acting on a point attack pick 88 6. Conclusions 97 6.1 Performance prediction model for impact hammer 97 6.2 Prediction model for specific energy required by point attack picks 99 6.3 Models for prediction of cutting, normal, and side force acting on a point attack pick 100 References 102 초 록 116 Appendix A 118 Acknowledgment 138박

Metaheuristic-Based Neural Network Training And Feature Selector For Intrusion Detection

Intrusion Detection (ID) in the context of computer networks is an essential technique in modern defense-in-depth security strategies. As such, Intrusion Detection Systems (IDSs) have received tremendous attention from security researchers and professionals. An important concept in ID is anomaly detection, which amounts to the isolation of normal behavior of network traffic from abnormal (anomaly) events. This isolation is essentially a classification task, which led researchers to attempt the application of well-known classifiers from the area of machine learning to intrusion detection. Neural Networks (NNs) are one of the most popular techniques to perform non-linear classification, and have been extensively used in the literature to perform intrusion detection. However, the training datasets usually compose feature sets of irrelevant or redundant information, which impacts the performance of classification, and traditional learning algorithms such as backpropagation suffer from known issues, including slow convergence and the trap of local minimum. Those problems lend themselves to the realm of optimization. Considering the wide success of swarm intelligence methods in optimization problems, the main objective of this thesis is to contribute to the improvement of intrusion detection technology through the application of swarm-based optimization techniques to the basic problems of selecting optimal packet features, and optimal training of neural networks on classifying those features into normal and attack instances. To realize these objectives, the research in this thesis follows three basic stages, succeeded by extensive evaluations

Evolution of Control Programs for a Swarm of Autonomous Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) are rapidly becoming a critical military asset. In the future, advances in miniaturization are going to drive the development of insect size UAVs. New approaches to controlling these swarms are required. The goal of this research is to develop a controller to direct a swarm of UAVs in accomplishing a given mission. While previous efforts have largely been limited to a two-dimensional model, a three-dimensional model has been developed for this project. Models of UAV capabilities including sensors, actuators and communications are presented. Genetic programming uses the principles of Darwinian evolution to generate computer programs to solve problems. A genetic programming approach is used to evolve control programs for UAV swarms. Evolved controllers are compared with a hand-crafted solution using quantitative and qualitative methods. Visualization and statistical methods are used to analyze solutions. Results indicate that genetic programming is capable of producing effective solutions to multi-objective control problems