3,578 research outputs found
Genetic and Swarm Algorithms for Optimizing the Control of Building HVAC Systems Using Real Data: A Comparative Study.
Buildings consume a considerable amount of electrical energy, the Heating, Ventilation,
and Air Conditioning (HVAC) system being the most demanding. Saving energy and maintaining
comfort still challenge scientists as they conflict. The control of HVAC systems can be improved by
modeling their behavior, which is nonlinear, complex, and dynamic and works in uncertain contexts.
Scientific literature shows that Soft Computing techniques require fewer computing resources
but at the expense of some controlled accuracy loss. Metaheuristics-search-based algorithms show
positive results, although further research will be necessary to resolve new challenging multi-objective
optimization problems. This article compares the performance of selected genetic and swarmintelligence-
based algorithms with the aim of discerning their capabilities in the field of smart buildings.
MOGA, NSGA-II/III, OMOPSO, SMPSO, and Random Search, as benchmarking, are compared
in hypervolume, generational distance, Δ-indicator, and execution time. Real data from the Building
Management System of Teatro Real de Madrid have been used to train a data model used for the
multiple objective calculations. The novelty brought by the analysis of the different proposed dynamic
optimization algorithms in the transient time of an HVAC system also includes the addition,
to the conventional optimization objectives of comfort and energy efficiency, of the coefficient of
performance, and of the rate of change in ambient temperature, aiming to extend the equipment
lifecycle and minimize the overshooting effect when passing to the steady state. The optimization
works impressively well in energy savings, although the results must be balanced with other real
considerations, such as realistic constraints on chillersâ operational capacity. The intuitive visualization
of the performance of the two families of algorithms in a real multi-HVAC system increases
the novelty of this proposal.post-print888 K
Improving the efficiency of Bayesian Network Based EDAs and their application in Bioinformatics
Estimation of distribution algorithms (EDAs) is a relatively new trend of stochastic optimizers which have received a lot of attention during last decade. In each generation, EDAs build probabilistic models of promising solutions of an optimization problem to guide the search process. New sets of solutions are obtained by sampling the corresponding probability distributions. Using this approach, EDAs are able to provide the user a set of models that reveals the dependencies between variables of the optimization problems while solving them. In order to solve a complex problem, it is necessary to use a probabilistic model which is able to capture the dependencies. Bayesian networks are usually used for modeling multiple dependencies between variables. Learning Bayesian networks, especially for large problems with high degree of dependencies among their variables is highly computationally expensive which makes it the bottleneck of EDAs. Therefore introducing efficient Bayesian learning algorithms in EDAs seems necessary in order to use them for large problems. In this dissertation, after comparing several Bayesian network learning algorithms, we propose an algorithm, called CMSS-BOA, which uses a recently introduced heuristic called max-min parent children (MMPC) in order to constrain the model search space. This algorithm does not consider a fixed and small upper bound on the order of interaction between variables and is able solve problems with large numbers of variables efficiently. We compare the efficiency of CMSS-BOA with the standard Bayesian network based EDA for solving several benchmark problems and finally we use it to build a predictor for predicting the glycation sites in mammalian proteins
Particle swarm optimization with sequential niche technique for dynamic finite element model updating
Peer reviewedPostprin
A Field Guide to Genetic Programming
xiv, 233 p. : il. ; 23 cm.Libro ElectrĂłnicoA Field Guide to Genetic Programming (ISBN 978-1-4092-0073-4) is an introduction to genetic programming (GP). GP is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. The authorsIntroduction --
Representation, initialisation and operators in Tree-based GP --
Getting ready to run genetic programming --
Example genetic programming run --
Alternative initialisations and operators in Tree-based GP --
Modular, grammatical and developmental Tree-based GP --
Linear and graph genetic programming --
Probalistic genetic programming --
Multi-objective genetic programming --
Fast and distributed genetic programming --
GP theory and its applications --
Applications --
Troubleshooting GP --
Conclusions.Contents
xi
1 Introduction
1.1 Genetic Programming in a Nutshell
1.2 Getting Started
1.3 Prerequisites
1.4 Overview of this Field Guide I
Basics
2 Representation, Initialisation and GP
2.1 Representation
2.2 Initialising the Population
2.3 Selection
2.4 Recombination and Mutation Operators in Tree-based
3 Getting Ready to Run Genetic Programming 19
3.1 Step 1: Terminal Set 19
3.2 Step 2: Function Set 20
3.2.1 Closure 21
3.2.2 Sufficiency 23
3.2.3 Evolving Structures other than Programs 23
3.3 Step 3: Fitness Function 24
3.4 Step 4: GP Parameters 26
3.5 Step 5: Termination and solution designation 27
4 Example Genetic Programming Run
4.1 Preparatory Steps 29
4.2 Step-by-Step Sample Run 31
4.2.1 Initialisation 31
4.2.2 Fitness Evaluation Selection, Crossover and Mutation Termination and Solution Designation Advanced Genetic Programming
5 Alternative Initialisations and Operators in
5.1 Constructing the Initial Population
5.1.1 Uniform Initialisation
5.1.2 Initialisation may Affect Bloat
5.1.3 Seeding
5.2 GP Mutation
5.2.1 Is Mutation Necessary?
5.2.2 Mutation Cookbook
5.3 GP Crossover
5.4 Other Techniques 32
5.5 Tree-based GP 39
6 Modular, Grammatical and Developmental Tree-based GP 47
6.1 Evolving Modular and Hierarchical Structures 47
6.1.1 Automatically Defined Functions 48
6.1.2 Program Architecture and Architecture-Altering 50
6.2 Constraining Structures 51
6.2.1 Enforcing Particular Structures 52
6.2.2 Strongly Typed GP 52
6.2.3 Grammar-based Constraints 53
6.2.4 Constraints and Bias 55
6.3 Developmental Genetic Programming 57
6.4 Strongly Typed Autoconstructive GP with PushGP 59
7 Linear and Graph Genetic Programming 61
7.1 Linear Genetic Programming 61
7.1.1 Motivations 61
7.1.2 Linear GP Representations 62
7.1.3 Linear GP Operators 64
7.2 Graph-Based Genetic Programming 65
7.2.1 Parallel Distributed GP (PDGP) 65
7.2.2 PADO 67
7.2.3 Cartesian GP 67
7.2.4 Evolving Parallel Programs using Indirect Encodings 68
8 Probabilistic Genetic Programming
8.1 Estimation of Distribution Algorithms 69
8.2 Pure EDA GP 71
8.3 Mixing Grammars and Probabilities 74
9 Multi-objective Genetic Programming 75
9.1 Combining Multiple Objectives into a Scalar Fitness Function 75
9.2 Keeping the Objectives Separate 76
9.2.1 Multi-objective Bloat and Complexity Control 77
9.2.2 Other Objectives 78
9.2.3 Non-Pareto Criteria 80
9.3 Multiple Objectives via Dynamic and Staged Fitness Functions 80
9.4 Multi-objective Optimisation via Operator Bias 81
10 Fast and Distributed Genetic Programming 83
10.1 Reducing Fitness Evaluations/Increasing their Effectiveness 83
10.2 Reducing Cost of Fitness with Caches 86
10.3 Parallel and Distributed GP are Not Equivalent 88
10.4 Running GP on Parallel Hardware 89
10.4.1 Masterâslave GP 89
10.4.2 GP Running on GPUs 90
10.4.3 GP on FPGAs 92
10.4.4 Sub-machine-code GP 93
10.5 Geographically Distributed GP 93
11 GP Theory and its Applications 97
11.1 Mathematical Models 98
11.2 Search Spaces 99
11.3 Bloat 101
11.3.1 Bloat in Theory 101
11.3.2 Bloat Control in Practice 104
III
Practical Genetic Programming
12 Applications
12.1 Where GP has Done Well
12.2 Curve Fitting, Data Modelling and Symbolic Regression
12.3 Human Competitive Results â the Humies
12.4 Image and Signal Processing
12.5 Financial Trading, Time Series, and Economic Modelling
12.6 Industrial Process Control
12.7 Medicine, Biology and Bioinformatics
12.8 GP to Create Searchers and Solvers â Hyper-heuristics xiii
12.9 Entertainment and Computer Games 127
12.10The Arts 127
12.11Compression 128
13 Troubleshooting GP
13.1 Is there a Bug in the Code?
13.2 Can you Trust your Results?
13.3 There are No Silver Bullets
13.4 Small Changes can have Big Effects
13.5 Big Changes can have No Effect
13.6 Study your Populations
13.7 Encourage Diversity
13.8 Embrace Approximation
13.9 Control Bloat
13.10 Checkpoint Results
13.11 Report Well
13.12 Convince your Customers
14 Conclusions
Tricks of the Trade
A Resources
A.1 Key Books
A.2 Key Journals
A.3 Key International Meetings
A.4 GP Implementations
A.5 On-Line Resources 145
B TinyGP 151
B.1 Overview of TinyGP 151
B.2 Input Data Files for TinyGP 153
B.3 Source Code 154
B.4 Compiling and Running TinyGP 162
Bibliography 167
Inde
Parallel surrogate-assisted global optimization with expensive functions â a survey
Surrogate assisted global optimization is gaining popularity. Similarly, modern advances in computing power increasingly rely on parallelization rather than faster processors. This paper examines some of the methods used to take advantage of parallelization in surrogate based global optimization. A key issue focused on in this review is how different algorithms balance exploration and exploitation. Most of the papers surveyed are adaptive samplers that employ Gaussian Process or Kriging surrogates. These allow sophisticated approaches for balancing exploration and exploitation and even allow to develop algorithms with calculable rate of convergence as function of the number of parallel processors. In addition to optimization based on adaptive sampling, surrogate assisted parallel evolutionary algorithms are also surveyed. Beyond a review of the present state of the art, the paper also argues that methods that provide easy parallelization, like multiple parallel runs, or methods that rely on population of designs for diversity deserve more attention.United States. Dept. of Energy (National Nuclear Security Administration. Advanced Simulation and Computing Program. Cooperative Agreement under the Predictive Academic Alliance Program. DE-NA0002378
Exploring parameter spaces with artificial intelligence and machine learning black-box optimisation algorithms
Constraining Beyond the Standard Model theories usually involves scanning
highly multi-dimensional parameter spaces and check observable predictions
against experimental bounds and theoretical constraints. Such task is often
timely and computationally expensive, especially when the model is severely
constrained and thus leading to very low random sampling efficiency. In this
work we tackled this challenge using Artificial Intelligence and Machine
Learning search algorithms used for Black-Box optimisation problems. Using the
cMSSM and the pMSSM parameter spaces, we consider both the Higgs mass and the
Dark Matter Relic Density constraints to study their sampling efficiency and
parameter space coverage. We find our methodology to produce orders of
magnitude improvement of sampling efficiency whilst reasonably covering the
parameter space.We thank JosĂ© Santiago PĂ©rez and Jorge RomĂŁo for the careful reading of the paper draft and for the useful discussions. This work is supported by FCT - Fundação para a CiĂȘncia e a Tecnologia, I.P. under project CERN/FIS-PAR/0024/2019. FAS is also supported by FCT under the research grant with reference UI/BD/153105/2022. The computational work was partially done using the resources made available by RNCA and INCD under project CPCA/A1/401197/2021info:eu-repo/semantics/publishedVersio
Symbolic Regression as Feature Engineering Method for Machine and Deep Learning Regression Tasks
In the realm of machine and deep learning regression tasks, the role of
effective feature engineering (FE) is pivotal in enhancing model performance.
Traditional approaches of FE often rely on domain expertise to manually design
features for machine learning models. In the context of deep learning models,
the FE is embedded in the neural network's architecture, making it hard for
interpretation. In this study, we propose to integrate symbolic regression (SR)
as an FE process before a machine learning model to improve its performance. We
show, through extensive experimentation on synthetic and real-world
physics-related datasets, that the incorporation of SR-derived features
significantly enhances the predictive capabilities of both machine and deep
learning regression models with 34-86% root mean square error (RMSE)
improvement in synthetic datasets and 4-11.5% improvement in real-world
datasets. In addition, as a realistic use-case, we show the proposed method
improves the machine learning performance in predicting superconducting
critical temperatures based on Eliashberg theory by more than 20% in terms of
RMSE. These results outline the potential of SR as an FE component in
data-driven models
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