Evolutionary models’ comparative analysis. Methodology proposition based on selected neo-schumpeterian models of industrial dynamics

A methodology of comparative analysis of evolutionary models is proposed. The main aim of this proposition is to identify to what extend different models can be called âevolutionary onesâ. Each model is analysed by searching for answers to following questions:  Is the model dynamical one?  Is it focused on far-from-equilibrium analysis?  What are a unit of evolution and a unit of selection?  Is diversity and heterogeneity of economic agents and their behaviour observed?  Is search for innovation based on a concept of hereditary information (knowledge)?  What kinds of innovation does the model describe?  Does selection process lead to diversified rate of growth and spontaneity of development?  How economic agents set prices?  What kind of products are described by the model?  Are decision making procedures and investment procedures present in the model? Outline of selected schumpeterian models is accompanied by identification of crucial evolutionary characteristics of each model and a short indication of phenomena explained by that model.Evolutionary economics, neo-schumpeterian models, simulation

Language Model Crossover: Variation through Few-Shot Prompting

This paper pursues the insight that language models naturally enable an intelligent variation operator similar in spirit to evolutionary crossover. In particular, language models of sufficient scale demonstrate in-context learning, i.e. they can learn from associations between a small number of input patterns to generate outputs incorporating such associations (also called few-shot prompting). This ability can be leveraged to form a simple but powerful variation operator, i.e. to prompt a language model with a few text-based genotypes (such as code, plain-text sentences, or equations), and to parse its corresponding output as those genotypes' offspring. The promise of such language model crossover (which is simple to implement and can leverage many different open-source language models) is that it enables a simple mechanism to evolve semantically-rich text representations (with few domain-specific tweaks), and naturally benefits from current progress in language models. Experiments in this paper highlight the versatility of language-model crossover, through evolving binary bit-strings, sentences, equations, text-to-image prompts, and Python code. The conclusion is that language model crossover is a promising method for evolving genomes representable as text

Natural Selection, Adaptive Evolution and Diversity in Computational Ecosystems

The central goal of this thesis is to provide additional criteria towards implementing open-ended evolution in an artificial system. Methods inspired by biological evolution are frequently applied to generate autonomous agents too complex to design by hand. Despite substantial progress in the area of evolutionary computation, additional efforts are needed to identify a coherent set of requirements for a system capable of exhibiting open-ended evolutionary dynamics. The thesis provides an extensive discussion of existing models and of the major considerations for designing a computational model of evolution by natural selection. Thus, the work in this thesis constitutes a further step towards determining the requirements for such a system and introduces a concrete implementation of an artificial evolution system to evaluate the developed suggestions. The proposed system improves upon existing models with respect to easy interpretability of agent behaviour, high structural freedom, and a low-level sensor and effector model to allow numerous long-term evolutionary gradients. In a series of experiments, the evolutionary dynamics of the system are examined against the set objectives and, where appropriate, compared with existing systems. Typical agent behaviours are introduced to convey a general overview of the system dynamics. These behaviours are related to properties of the respective agent populations and their evolved morphologies. It is shown that an intuitive classification of observed behaviours coincides with a more formal classification based on morphology. The evolutionary dynamics of the system are evaluated and shown to be unbounded according to the classification provided by Bedau and Packard’s measures of evolutionary activity. Further, it is analysed how observed behavioural complexity relates to the complexity of the agent-side mechanisms subserving these behaviours. It is shown that for the concrete definition of complexity applied, the average complexity continually increases for extended periods of evolutionary time. In combination, these two findings show how the observed behaviours are the result of an ongoing and lasting adaptive evolutionary process as opposed to being artifacts of the seeding process. Finally, the effect of variation in the system on the diversity of evolved behaviour is investigated. It is shown that coupling individual survival and reproductive success can restrict the available evolutionary trajectories in more than the trivial sense of removing another dimension, and conversely, decoupling individual survival from reproductive success can increase the number of evolutionary trajectories. The effect of different reproductive mechanisms is contrasted with that of variation in environmental conditions. The diversity of evolved strategies turns out to be sensitive to the reproductive mechanism while being remarkably robust to the variation of environmental conditions. These findings emphasize the importance of being explicit about the abstractions and assumptions underlying an artificial evolution system, particularly if the system is intended to model aspects of biological evolution

Performance assessment of Surrogate model integrated with sensitivity analysis in multi-objective optimization

This Thesis develops a new multi-objective heuristic algorithm. The optimum searching task is performed by a standard genetic algorithm. Furthermore, it is assisted by the Response Surface Methodology surrogate model and by two sensitivity analysis methods: the Variance-based, also known as Sobol’ analysis, and the Elementary Effects. Once built the entire method, it is compared on several multi-objective problems with some other algorithms

Automatic machine learning:methods, systems, challenges

This open access book presents the first comprehensive overview of general methods in Automatic Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first international challenge of AutoML systems. The book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. Many of the recent machine learning successes crucially rely on human experts, who select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters; however the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself

New design companions opening up the process through self-made computation

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Architecture, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 73-75).This thesis is about man and machine roles in the early conception of designs where it investigates computational methods that support creativity and surprise. It discusses the relationship between human and digital medium in the enterprise of Computer-Aided Design', and Self-Made Computation to empower the designer as driver of digital processes taking the computer as an active collaborator, or a sharp apprentice, rather than a master. In a design process tool personalization enables precise feedback between human and medium. In the field of architecture, every project is unique, and there are as many design workflows as designers. However current off-the-shelf design software has an inflexible built-in structure targeting general problem-solving that can interfere with non-standard design needs. Today, those with programming agility look for customized processes that assist early problem-finding instead of converging solutions. Contributing to alleviate software frustrations, smaller tailor-made applications prove to be precisely tailored, viable and enriching companions in certain moments of the project development. Previous work on the impact of standardized software for design has focused on the figure of the designer as a tool-user, this thesis addresses the question from the vision of the designer as a tool-maker. It investigates how self-made software can become a design companion for computational thinking - observed here as a new mindset that shifts design workflows, rather than a technique. The research compares and diagrams designer-toolmaker work where self-made applets where produced, as well as the structures in the work of rule-maker artisans. The main contributions are a comparative study of three models of computer-aided design, their history and technical review, their influence in design workflows and a graphical framework to better compare them. Critical analysis reveals a common structure to tailor a creative and explorative design workflow. Its advantages and limitations are exposed to guide designers into alternative computational methods for design processes. Keywords: design workflow; computation; applets; self-made tools; diagrams; design process; feedback; computers; computer-assisted-designby Laia Mogas-Soldevila.S.M

Evolutionary design of deep neural networks

Mención Internacional en el título de doctorFor three decades, neuroevolution has applied evolutionary computation to the optimization of the topology of artificial neural networks, with most works focusing on very simple architectures. However, times have changed, and nowadays convolutional neural networks are the industry and academia standard for solving a variety of problems, many of which remained unsolved before the discovery of this kind of networks. Convolutional neural networks involve complex topologies, and the manual design of these topologies for solving a problem at hand is expensive and inefficient. In this thesis, our aim is to use neuroevolution in order to evolve the architecture of convolutional neural networks. To do so, we have decided to try two different techniques: genetic algorithms and grammatical evolution. We have implemented a niching scheme for preserving the genetic diversity, in order to ease the construction of ensembles of neural networks. These techniques have been validated against the MNIST database for handwritten digit recognition, achieving a test error rate of 0.28%, and the OPPORTUNITY data set for human activity recognition, attaining an F1 score of 0.9275. Both results have proven very competitive when compared with the state of the art. Also, in all cases, ensembles have proven to perform better than individual models. Later, the topologies learned for MNIST were tested on EMNIST, a database recently introduced in 2017, which includes more samples and a set of letters for character recognition. Results have shown that the topologies optimized for MNIST perform well on EMNIST, proving that architectures can be reused across domains with similar characteristics. In summary, neuroevolution is an effective approach for automatically designing topologies for convolutional neural networks. However, it still remains as an unexplored field due to hardware limitations. Current advances, however, should constitute the fuel that empowers the emergence of this field, and further research should start as of today.This Ph.D. dissertation has been partially supported by the Spanish Ministry of Education, Culture and Sports under FPU fellowship with identifier FPU13/03917. This research stay has been partially co-funded by the Spanish Ministry of Education, Culture and Sports under FPU short stay grant with identifier EST15/00260.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: María Araceli Sanchís de Miguel.- Secretario: Francisco Javier Segovia Pérez.- Vocal: Simon Luca

Tools for discovering and characterizing extrasolar planets

Among the group of extrasolar planets, transiting planets provide a great opportunity to obtain direct measurements for the basic physical properties, such as mass and radius of these objects. These planets are therefore highly important in the understanding of the evolution and formation of planetary systems: from the observations of photometric transits, the interior structure of the planet and atmospheric properties can also be constrained. The most efficient way to search for transiting extrasolar planets is based on wide-field surveys by hunting for short and shallow periodic dips in light curves covering quite long time intervals. These surveys monitor fields with several degrees in diameter and tens or hundreds of thousands of objects simultaneously. In the practice of astronomical observations, surveys of large field-of-view are rather new and therefore require special methods for photometric data reduction that have not been used before. In this PhD thesis, I summarize my efforts related to the development of a complete software solution for high precision photometric reduction of astronomical images. I also demonstrate the role of this newly developed package and the related algorithms in the case of particular discoveries of the HATNet project. [abridged]Comment: PhD thesis, Eotvos Lorand University (June 18, 2009), 68 pages in journal style, 41 figures, 18 table

Contributions to the analysis and segmentation of remote sensing hyperspectral images

142 p.This PhD Thesis deals with the segmentation of hyperspectral images from the point of view of Lattice Computing. We have introduced the application of Associative Morphological Memories as a tool to detect strong lattice independence, which has been proven equivalent to affine independence. Therefore, sets of strong lattice independent vectors found using our algorithms correspond to the vertices of convex sets that cover most of the data. Unmixing the data relative to these endmembers provides a collection of abundance images which can be assumed either as unsupervised segmentations of the images or as features extracted from the hyperspectral image pixels. Besides, we have applied this feature extraction to propose a content based image retrieval approach based on the image spectral characterization provided by the endmembers. Finally, we extended our ideas to the proposal of Morphological Cellular Automata whose dynamics are guided by the morphological/lattice independence properties of the image pixels. Our works have also explored the applicability of Evolution Strategies to the endmember induction from the hyperspectral image data