Cultural Algorithm based on Decomposition to solve Optimization Problems

Decomposition is used to solve optimization problems by introducing many simple scalar optimization subproblems and optimizing them simultaneously. Dynamic Multi-Objective Optimization Problems (DMOP) have several objective functions and constraints that vary over time. As a consequence of such dynamic changes, the optimal solutions may vary over time, affecting the performance of convergence. In this thesis, we propose a new Cultural Algorithm (CA) based on decomposition (CA/D). The objective of the CA/D algorithm is to decompose DMOP into a number of subproblems that can be optimized using the information shared by neighboring problems. The proposed CA/D approach is evaluated using a number of CEC 2015 optimization benchmark functions. When compared to CA, Multi-population CA (MPCA), and MPCA incorporating game strategies (MPCA-GS), the results obtained showed that CA/D outperformed them in 7 out of the 15 benchmark functions

Evolutionary Deep Reinforcement Learning Using Elite Buffer: A Novel Approach Towards DRL Combined with EA in Continuous Control Tasks

Despite the numerous applications and success of deep reinforcement learning in many control tasks, it still suffers from many crucial problems and limitations, including temporal credit assignment with sparse reward, absence of effective exploration, and a brittle convergence that is extremely sensitive to the hyperparameters of the problem. The problems of deep reinforcement learning in continuous control, along with the success of evolutionary algorithms in facing some of these problems, have emerged the idea of evolutionary reinforcement learning, which attracted many controversies. Despite successful results in a few studies in this field, a proper and fitting solution to these problems and their limitations is yet to be presented. The present study aims to study the efficiency of combining the two fields of deep reinforcement learning and evolutionary computations further and take a step towards improving methods and the existing challenges. The "Evolutionary Deep Reinforcement Learning Using Elite Buffer" algorithm introduced a novel mechanism through inspiration from interactive learning capability and hypothetical outcomes in the human brain. In this method, the utilization of the elite buffer (which is inspired by learning based on experience generalization in the human mind), along with the existence of crossover and mutation operators, and interactive learning in successive generations, have improved efficiency, convergence, and proper advancement in the field of continuous control. According to the results of experiments, the proposed method surpasses other well-known methods in environments with high complexity and dimension and is superior in resolving the mentioned problems and limitations

User hints for optimisation processes

Innovative improvements in the area of Human-Computer Interaction and User Interfaces have en-abled intuitive and effective applications for a variety of problems. On the other hand, there has also been the realization that several real-world optimization problems still cannot be totally auto-mated. Very often, user interaction is necessary for refining the optimization problem, managing the computational resources available, or validating or adjusting a computer-generated solution. This thesis investigates how humans can help optimization methods to solve such difficult prob-lems. It presents an interactive framework where users play a dynamic and important role by pro-viding hints. Hints are actions that help to insert domain knowledge, to escape from local minima, to reduce the space of solutions to be explored, or to avoid ambiguity when there is more than one optimal solution. Examples of user hints are adjustments of constraints and of an objective function, focusing automatic methods on a subproblem of higher importance, and manual changes of an ex-isting solution. User hints are given in an intuitive way through a graphical interface. Visualization tools are also included in order to inform about the state of the optimization process. We apply the User Hints framework to three combinatorial optimization problems: Graph Clus-tering, Graph Drawing and Map Labeling. Prototype systems are presented and evaluated for each problem. The results of the study indicate that optimization processes can benefit from human interaction. The main goal of this thesis is to list cases where human interaction is helpful, and provide an ar-chitecture for supporting interactive optimization. Our contributions include the general User Hints framework and particular implementations of it for each optimization problem. We also present a general process, with guidelines, for applying our framework to other optimization problems

Interactive evolutionary 3D fractal modeling.

Pang, Wenjun.Thesis (M.Phil.)--Chinese University of Hong Kong, 2009.Includes bibliographical references (leaves 83-88).Abstracts in English and Chinese.ACKNOWLEDGEMENTS --- p.iiABSTRACT --- p.iv摘要 --- p.vCONTENTS --- p.viList of Tables --- p.viiiList of Figures --- p.ixChapter 1. --- INTRODUCTION --- p.1Chapter 1.1 --- Recent research work --- p.4Chapter 1.2 --- Objectives --- p.8Chapter 1.3 --- Thesis Organization --- p.10Chapter 2. --- FRACTAL MODELING --- p.12Chapter 2.1 --- Fractal and Fractal Art --- p.12Chapter 2.2 --- Fractal Geometry --- p.15Chapter 2.3 --- Construction of Fractals --- p.21Chapter 2.4 --- Fractal Measurement and Aesthetics --- p.27Chapter 3. --- OVERVIEW OF EVOLUTIONARY DESIGN --- p.30Chapter 3.1 --- Initialization --- p.33Chapter 3.2 --- Selection --- p.33Chapter 3.3 --- Reproduction --- p.34Chapter 3.4 --- Termination --- p.36Chapter 4. --- EVOLUTIONARY 3D FRACTAL MODELING --- p.38Chapter 4.1 --- Fractal Construction --- p.38Chapter 4.1.1 --- Self-similar Condition of Fractal --- p.38Chapter 4.1.2 --- Fractal Transformation (FT) IFS Formulation --- p.39Chapter 4.1.3 --- IFS Genotype and Phenotype Expression --- p.41Chapter 4.2 --- Evolutionary Algorithm --- p.43Chapter 4.2.1 --- Single-point Crossover --- p.45Chapter 4.2.2 --- Arithmetic Gaussian mutation --- p.45Chapter 4.2.3 --- Inferior Elimination --- p.46Chapter 4.3 --- Interactive Fine-tuning using FT IFS --- p.46Chapter 4.4 --- Gaussian Fitness Function --- p.48Chapter 5. --- GAUSSIAN AESTHETIC FITNESS FUNCTION --- p.49Chapter 5.1 --- Fitness Considerations --- p.50Chapter 5.2 --- Fitness Function Formulation --- p.53Chapter 5.3 --- Results and Discussion on Fitness Function --- p.55Chapter 6. --- EXPERIMENT RESULTS and DISCUSSION --- p.59Chapter 6.1 --- Experiment of Evolutionary Generation --- p.59Chapter 6.2 --- Comparison on Different Methods --- p.60Chapter 7. --- 3D FRACTALS RENDERING and APPLICATION --- p.62Chapter 7.1 --- Transforming Property and User Modification --- p.62Chapter 7.2 --- Visualization and Rendering of 3D Fractals --- p.66Chapter 7.3 --- Applications in Design --- p.74Chapter 8. --- CONCLUSIONS and FUTURE WORK --- p.81Chapter 8.1 --- Conclusions --- p.81Chapter 8.2 --- Future Work --- p.81BIBLIOGRAPHY --- p.83Appendix --- p.89Marching Cubes Method --- p.8

Adaptive augmented evolutionary intelligence for the design of water distribution networks

This is the author accepted manuscript. The final version is available from ACM via the DOI in this recordGenetic and Evolutionary Computation Conference (GECCO ’20), 8-12 July 2020, Cancún, MexicoThe application of Evolutionary Algorithms (EAs) to realworld problems comes with inherent challenges, primarily the difficulty in defining the large number of considerations needed when designing complex systems such as Water Distribution Networks (WDN). One solution is to use an Interactive Evolutionary Algorithm (IEA), which integrates a human expert into the optimisation process and helps guide it to solutions more suited to real-world application. The involvement of an expert provides the algorithm with valuable domain knowledge; however, it is an intensive task requiring extensive interaction, leading to user fatigue and reduced effectiveness. To address this, the authors have developed methods for capturing human expertise from user interactions utilising machine learning to produce Human-Derived Heuristics (HDH) which are integrated into an EA’s mutation operator. This work focuses on the development of an adaptive method for applying multiple HDHs throughout an EA’s search. The new adaptive approach is shown to outperform both singular HDH approaches and traditional EAs on a range of large scale WDN design problems. This work paves the way for the development of a new type of IEA that has the capability of learning from human experts whilst minimising user fatigue.Engineering and Physical Sciences Research Council (EPSRC

Evolutionary design assistants for architecture

In its parallel pursuit of an increased competitivity for design offices and more pleasurable and easier workflows for designers, artificial design intelligence is a technical, intellectual, and political challenge. While human-machine cooperation has become commonplace through Computer Aided Design (CAD) tools, a more improved collaboration and better support appear possible only through an endeavor into a kind of artificial design intelligence, which is more sensitive to the human perception of affairs. Considered as part of the broader Computational Design studies, the research program of this quest can be called Artificial / Autonomous / Automated Design (AD). The current available level of Artificial Intelligence (AI) for design is limited and a viable aim for current AD would be to develop design assistants that are capable of producing drafts for various design tasks. Thus, the overall aim of this thesis is the development of approaches, techniques, and tools towards artificial design assistants that offer a capability for generating drafts for sub-tasks within design processes. The main technology explored for this aim is Evolutionary Computation (EC), and the target design domain is architecture. The two connected research questions of the study concern, first, the investigation of the ways to develop an architectural design assistant, and secondly, the utilization of EC for the development of such assistants. While developing approaches, techniques, and computational tools for such an assistant, the study also carries out a broad theoretical investigation into the main problems, challenges, and requirements towards such assistants on a rather overall level. Therefore, the research is shaped as a parallel investigation of three main threads interwoven along several levels, moving from a more general level to specific applications. The three research threads comprise, first, theoretical discussions and speculations with regard to both existing literature and the proposals and applications of the thesis; secondly, proposals for descriptive and prescriptive models, mappings, summary illustrations, task structures, decomposition schemes, and integratory frameworks; and finally, experimental applications of these proposals. This tripartite progression allows an evaluation of each proposal both conceptually and practically; thereby, enabling a progressive improvement of the understanding regarding the research question, while producing concrete outputs on the way. Besides theoretical and interpretative examinations, the thesis investigates its subject through a set of practical and speculative proposals, which function as both research instruments and the outputs of the study. The first main output of the study is the “design_proxy” approach (d_p), which is an integrated approach for draft making design assistants. It is an outcome of both theoretical examinations and experimental applications, and proposes an integration of, (1) flexible and relaxed task definitions and representations (instead of strict formalisms), (2) intuitive interfaces that make use of usual design media, (3) evaluation of solution proposals through their similarity to given examples, and (4) a dynamic evolutionary approach for solution generation. The design_proxy approach may be useful for AD researchers that aim at developing practical design assistants, as has been examined and demonstrated with the two applications, i.e., design_proxy.graphics and design_proxy.layout. The second main output, the “Interleaved Evolutionary Algorithm” (IEA, or Interleaved EA) is a novel evolutionary algorithm proposed and used as the underlying generative mechanism of design_proxybased design assistants. The Interleaved EA is a dynamic, adaptive, and multi-objective EA, in which one of the objectives leads the evolution until its fitness progression stagnates; in the sense that the settings and fitness values of this objective is used for most evolutionary decisions. In this way, the Interleaved EA enables the use of different settings and operators for each of the objectives within an overall task, which would be the same for all objectives in a regular multi-objective EA. This property gives the algorithm a modular structure, which offers an improvable method for the utilization of domain-specific knowledge for each sub-task, i.e., objective. The Interleaved EA can be used by Evolutionary Computation (EC) researchers and by practitioners who employ EC for their tasks. As a third main output, the “Architectural Stem Cells Framework” is a conceptual framework for architectural design assistants. It proposes a dynamic and multi-layered method for combining a set of design assistants for larger tasks in architectural design. The first component of the framework is a layer-based, parallel task decomposition approach, which aims at obtaining a dynamic parallelization of sub-tasks within a more complicated problem. The second component of the framework is a conception for the development mechanisms for building drafts, i.e., Architectural Stem Cells (ASC). An ASC can be conceived as a semantically marked geometric structure, which contains the information that specifies the possibilities and constraints for how an abstract building may develop from an undetailed stage to a fully developed building draft. ASCs are required for re-integrating the separated task layers of an architectural problem through solution-based development. The ASC Framework brings together many of the ideas of this thesis for a practical research agenda and it is presented to the AD researchers in architecture. Finally, the “design_proxy.layout” (d_p.layout) is an architectural layout design assistant based on the design_proxy approach and the IEA. The system uses a relaxed problem definition (producing draft layouts) and a flexible layout representation that permits the overlapping of design units and boundaries. User interaction with the system is carried out through intuitive 2D graphics and the functional evaluations are performed by measuring the similarity of a proposal to existing layouts. Functioning in an integrated manner, these properties make the system a practicable and enjoying design assistant, which was demonstrated through two workshop cases. The d_p.layout is a versatile and robust layout design assistant that can be used by architects in their design processes