Artificial Immune System for Solving Global Optimization Problems

In this paper, we present a novel model of an artificial immune system (AIS), based on the process that suffers the T-Cell. The proposed model is used for global optimization problems. The model operates on four populations: Virgins, Effectors (CD4 and CD8) and Memory. Each of them has a different role, representation and procedures. We validate our proposed approach with a set of test functions taken from the specialized literature, we also compare our results with the results obtained by different bio-inspired approaches and we statistically analyze the results gotten by our approach.Fil: Aragon, Victoria Soledad. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo En Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Luis; ArgentinaFil: Esquivel, Susana C.. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; ArgentinaFil: Coello Coello, Carlos A.. CINVESTAV-IPN; Méxic

Optimization on industrial problems focussing on multi-player strategies

Algorithms (EA) are useful optimization methods for exploration of the search space, but they usually have slowness problems to exploit and converge to the minimum with accuracy. On the other hand, gradient based methods converge faster to local minimums, although are not so robust (e.g., flat areas and discontinuities can cause problems) and they lack exploration capabilities. This thesis presents and analyze four versions of a hybrid optimization method trying to combine the virtues of Evolutionary Algorithms (EA) and gradient based algorithms, and to overcome their corresponding drawbacks. The proposed Hybrid Methods enables working with N optimization algorithms (called players), multiple objective functions and design variables, and define them differently for each player. The performance of the Hybrid Methods are compared against a gradient based method, two Genetic Algorithms (GA) and a Particle Swarm Optimization (PSO). Tests have been conducted with mathematical benchmark problems (synthetic tests designed to specifically test optimization methods) and an engineering application with high demanding computational resources, a Synthetic Jet actuator for Active Flow Control (AFC) over a 2D Selig-Donovan 7003 (SD7003) airfoil at Reynolds number 6 x 10^4 and a 14 degree angle of attack. The Active Flow control problem has been used in a single optimization problem and in a two objective optimization problemEls Algoritmes Evolutius (EA) són mètodes d'optimització útils per a l'exploració de l'espai de cerca, però solen tenir problemes de lentitud per explotar-ne el mínim i convergir amb precisió. D'altra banda, els mètodes basats en gradients convergeixen més ràpidament als mínims locals, encara que no són tan robusts (per exemple, les àrees planes i les discontinuïtats poden causar problemes) i no tenen capacitats d'exploració. Aquesta tesi presenta i analitza quatre versions d'un mètode d'optimització híbrid que intenta combinar les virtuts dels Algoritmes Evolutius (EA) i els algoritmes basats en gradients, i superar-ne els inconvenients corresponents. Els Mètodes Híbrids proposats permeten treballar amb N algoritmes d'optimització (anomenats jugadors), múltiples funcions objectiu i variables de disseny, i definir-les de manera diferent per a cada jugador. El rendiment dels mètodes híbrids es compara amb un mètode basat en gradient, dos Algoritmes Genètics (GA) i un mètode d'optimització d'eixam de partícules (PSO). S'han fet proves amb problemes matemàtics de referència (proves sintètiques dissenyades per provar específicament mètodes d'optimització) i una aplicació d'enginyeria amb recursos computacionals molt exigents, un actuador de jet sintètic per a control de flux actiu (AFC) sobre un perfil aerodinàmic 2D Selig -Donovan 7003 (SD7003) al número de Reynolds 6 x 104 i un angle d'atac de 14 graus. El problema de control de flux actiu s'ha utilitzat en un problema d'optimització monoobjectiu i en un problema d'optimització de dos objectius.Postprint (published version

UNDERSTANDING THE IMPLICATIONS OF LINEAGE PLASTICITY IN BREAST CANCER EVOLUTION AND CHEMOTHERAPY RESPONSE

Intra-tumoral heterogeneity and the presence of a phenotypically diverse cell population within a single tumor represents a major hurdle in the understanding of tumor progression and dynamics, and complicates the effective diagnosis and management of this disease. One of the ways by which tumors gain intra-tumoral variation is through the acquisition of phenotypic or lineage plasticity, whereby tumor cells evolve away from the lineage of origin and gain altered profiles. These alterations may impart specific survival benefits to different subpopulations of cells, enabling them to proliferate faster, migrate away from the site of the primary tumor or evade drug-induced elimination, amongst others. Phenotypic plasticity and alterations of transcriptional profiles can be driven by either extrinsic signals or intrinsic cell autonomous mechanisms. Work presented in this thesis across three chapters has uncovered several molecular drivers altering cell-state plasticity in breast cancer, and their resulting effects on tumor development and progression. Lineage plasticity, driven by the transcription factor SOX10, allows breast tumors of the luminal lineage expressing lower Estrogen Receptor (ER) levels to gain basal-like characteristics, resulting in the evolution of these luminal-like tumors into a more basal-like subtype. Activation of Protein Kinase A (PKA) curtails cellular plasticity in a mouse mammary tumor model, preventing epithelial-mesenchymal transition (EMT) and metastasis, ultimately improving prognosis and survival. Eribulin treatment induces transcriptional reprogramming of breast tumor cell lines, forcing them to undergo mesenchymal-epithelial transition (MET). Together, these findings help to elucidate how cellular plasticity contributes to intra-tumoral heterogeneity of breast tumors, and how phenotypic diversity influences the progression, metastasis, and chemotherapy response of breast cancer. While these results have identified specific agents that act to promote phenotypic plasticity, the exact mechanisms by which they act, and the steps necessary for lineage evolution to occur are only partially understood. This work provides a foundation for further inquiry into the mechanisms driving phenotypic plasticity and resulting tumor heterogeneity, with the ultimate goal of developing better strategies to overcome this disease

Quantum-Inspired Distributed Memetic Algorithm

This paper proposed a novel distributed memetic evolutionary model, where four modules distributed exploration, intensified exploitation, knowledge transfer, and evolutionary restart are coevolved to maximize their strengths and achieve superior global optimality. Distributed exploration evolves three independent populations by heterogenous operators. Intensified exploitation evolves an external elite archive in parallel with exploration to balance global and local searches. Knowledge transfer is based on a point-ring communication topology to share successful experiences among distinct search agents. Evolutionary restart adopts an adaptive perturbation strategy to control search diversity reasonably. Quantum computation is a newly emerging technique, which has powerful computing power and parallelized ability. Therefore, this paper further fuses quantum mechanisms into the proposed evolutionary model to build a new evolutionary algorithm, referred to as quantum-inspired distributed memetic algorithm (QDMA). In QDMA, individuals are represented by the quantum characteristics and evolved by the quantum-inspired evolutionary optimizers in the quantum hyperspace. The QDMA integrates the superiorities of distributed, memetic, and quantum evolution. Computational experiments are carried out to evaluate the superior performance of QDMA. The results demonstrate the effectiveness of special designs and show that QDMA has greater superiority compared to the compared state-of-the-art algorithms based on Wilcoxon’s rank-sum test. The superiority is attributed not only to good cooperative coevolution of distributed memetic evolutionary model, but also to superior designs of each special component

The germinal centre artificial immune system

This thesis deals with the development and evaluation of the Germinal centre artificial immune system (GC-AIS) which is a novel artificial immune system based on advancements in the understanding of the germinal centre reaction of the immune system. The key research questions addressed in this thesis are: can an artificial immune system (AIS) be designed by taking inspiration from recent developments in immunology to tackle multi-objective optimisation problems? How can we incorporate desirable features of the immune system like diversity, parallelism and memory into this proposed AIS? How does the proposed AIS compare with other state of the art techniques in the field of multi-objective optimisation problems? How can we incorporate the learning component of the immune system into the algorithm and investigate the usefulness of memory in dynamic scenarios? The main contributions of the thesis are: • Understanding the behaviour and performance of the proposed GC-AIS on multiobjective optimisation problems and explaining its benefits and drawbacks, by comparing it with simple baseline and state of the art algorithms. • Improving the performance of GC-AIS by incorporating a popular technique from multi-objective optimisation. By overcoming its weaknesses the capability of the improved variant to compete with the state of the art algorithms is evaluated. • Answering key questions on the usefulness of incorporating memory in GC-AIS in a dynamic scenario

Sociobiology, universal Darwinism and their transcendence: An investigation of the history, philosophy and critique of Darwinian paradigms, especially gene-Darwinism, process-Darwinism, and their types of reductionism towards a theory of the evolution of evolutionary processes, evolutionary freedom and ecological idealism

Based on a review of different Darwinian paradigms, particularly sociobiology, this work, both, historically and philosophically, develops a metaphysic of gene-Darwinism and process-Darwinism, and then criticises and transcends these Darwinian paradigms in order to achieve a truly evolutionary theory of evolution. Part I introduces essential aspects of current sociobiology as the original challenge to this investigation. The claim of some sociobiologists that ethics should become biologized in a gene-egoistic way, is shown to be tied to certain biological views, which ethically lead to problematic results. In part II a historical investigation into sociobiology and Darwinism in general provides us, as historical epistemology', with a deeper understanding of the structure and background of these approaches. Gene-Darwinism, which presently dominates sociobiology and is linked to Dawkins' selfish gene view of evolution, is compared to Darwin's Darwinism and the evolutionary' synthesis and becomes defined more strictly. An account of the external history of Darwinism and its subparadigms shows how cultural intellectual presuppositions, like Malthusianism or the Newtonian concept of the unchangeable laws of nature, also influenced biological theory' construction. In part III universal 'process-Darwinism' is elaborated based on the historical interaction of Darwinism with non-biological subject areas. Building blocks for this are found in psychology, the theory of science and economics. Additionally, a metaphysical argument for the universality of process- Darwinism, linked to Hume's and Popper's problem of induction, is proposed. In part IV gene-Darwinism and process-Darwinism are criticised. Gene-Darwinism—despite its merits—is challenged as being one-sided in advocating 'gene-atomism', 'germ-line reductionism' and 'process-monism'. My alternative proposals develop and try to unify different criticisms often found. In respect of gene-atomism I advocate a many-level approach, opposing the necessary radical selfishness of single genes. I develop the concept of higher-level genes, propose a concept of systemic selection, which may stabilise group properties, without relying on permanent group selection and extend the applicability of a certain group selectionist model generally to small open groups. Proposals of mine linked to the critique of germ-line reductionism are: 'exformation', phenotypes as evolutionary factors and a field theoretic understanding of causa formalis (resembling Aristotelian hylemorphism). Finally the process-monism of gene-Darwinism, process-Darwinism and, if defined strictly, Darwinism in general is criticised. 1 argue that our ontology and ethics would be improved by replacing the Newtoman-Paleyian deist metaphor of an eternal and unchangeable law of nature, which lies at tire very heart of Darwinism, by a truly evolutionary understanding of evolution where new processes may gain a certain autonomy. All this results in a view that I call 'ecological idealism', which, although still very much based on Darwinism, clearly transcends a Darwinian world view