The Emergence of Canalization and Evolvability in an Open-Ended, Interactive Evolutionary System

Natural evolution has produced a tremendous diversity of functional organisms. Many believe an essential component of this process was the evolution of evolvability, whereby evolution speeds up its ability to innovate by generating a more adaptive pool of offspring. One hypothesized mechanism for evolvability is developmental canalization, wherein certain dimensions of variation become more likely to be traversed and others are prevented from being explored (e.g. offspring tend to have similarly sized legs, and mutations affect the length of both legs, not each leg individually). While ubiquitous in nature, canalization almost never evolves in computational simulations of evolution. Not only does that deprive us of in silico models in which to study the evolution of evolvability, but it also raises the question of which conditions give rise to this form of evolvability. Answering this question would shed light on why such evolvability emerged naturally and could accelerate engineering efforts to harness evolution to solve important engineering challenges. In this paper we reveal a unique system in which canalization did emerge in computational evolution. We document that genomes entrench certain dimensions of variation that were frequently explored during their evolutionary history. The genetic representation of these organisms also evolved to be highly modular and hierarchical, and we show that these organizational properties correlate with increased fitness. Interestingly, the type of computational evolutionary experiment that produced this evolvability was very different from traditional digital evolution in that there was no objective, suggesting that open-ended, divergent evolutionary processes may be necessary for the evolution of evolvability.Comment: SI can be found at: http://www.evolvingai.org/files/SI_0.zi

Fitness Proportionate Niching: Harnessing The Power Of Evolutionary Algorithms For Evolving Cooperative Populations And Dynamic Clustering

Evolutionary algorithms work on the notion of best fit will survive criteria. This makes evolving a cooperative and diverse population in a competing environment via evolutionary algorithms a challenging task. Analogies to species interactions in natural ecological systems have been used to develop methods for maintaining diversity in a population. One such area that mimics species interactions in natural systems is the use of niching. Niching methods extend the application of EAs to areas that seeks to embrace multiple solutions to a given problem. The conventional fitness sharing technique has limitations when the multimodal fitness landscape has unequal peaks. Higher peaks are strong population attractors. And this technique suffers from the curse of population size in attempting to discover all optimum points. The use of high population size makes the technique computationally complex, especially when there is a big jump in fitness values of the peaks. This work introduces a novel bio-inspired niching technique, termed Fitness Proportionate Niching (FPN), based on the analogy of finite resource model where individuals share the resource of a niche in proportion to their actual fitness. FPN makes the search algorithm unbiased to the variation in fitness values of the peaks and hence mitigates the drawbacks of conventional fitness sharing. FPN extends the global search ability of Genetic Algorithms (GAs) for evolving hierarchical cooperation in genetics-based machine learning and dynamic clustering. To this end, this work introduces FPN based resource sharing which leads to the formation of a viable default hierarchy in classifiers for the first time. It results in the co-evolution of default and exception rules, which lead to a robust and concise model description. The work also explores the feasibility and success of FPN for dynamic clustering. Unlike most other clustering techniques, FPN based clustering does not require any a priori information on the distribution of the data

Emergent Behavior Development and Control in Multi-Agent Systems

Emergence in natural systems is the development of complex behaviors that result from the aggregation of simple agent-to-agent and agent-to-environment interactions. Emergence research intersects with many disciplines such as physics, biology, and ecology and provides a theoretical framework for investigating how order appears to spontaneously arise in complex adaptive systems. In biological systems, emergent behaviors allow simple agents to collectively accomplish multiple tasks in highly dynamic environments; ensuring system survival. These systems all display similar properties: self-organized hierarchies, robustness, adaptability, and decentralized task execution. However, current algorithmic approaches merely present theoretical models without showing how these models actually create hierarchical, emergent systems. To fill this research gap, this dissertation presents an algorithm based on entropy and speciation - defined as morphological or physiological differences in a population - that results in hierarchical emergent phenomena in multi-agent systems. Results show that speciation creates system hierarchies composed of goal-aligned entities, i.e. niches. As niche actions aggregate into more complex behaviors, more levels emerge within the system hierarchy, eventually resulting in a system that can meet multiple tasks and is robust to environmental changes. Speciation provides a powerful tool for creating goal-aligned, decentralized systems that are inherently robust and adaptable, meeting the scalability demands of current, multi-agent system design. Results in base defense, k-n assignment, division of labor and resource competition experiments, show that speciated populations create hierarchical self-organized systems, meet multiple tasks and are more robust to environmental change than non-speciated populations

Digital Ecosystems: Ecosystem-Oriented Architectures

We view Digital Ecosystems to be the digital counterparts of biological ecosystems. Here, we are concerned with the creation of these Digital Ecosystems, exploiting the self-organising properties of biological ecosystems to evolve high-level software applications. Therefore, we created the Digital Ecosystem, a novel optimisation technique inspired by biological ecosystems, where the optimisation works at two levels: a first optimisation, migration of agents which are distributed in a decentralised peer-to-peer network, operating continuously in time; this process feeds a second optimisation based on evolutionary computing that operates locally on single peers and is aimed at finding solutions to satisfy locally relevant constraints. The Digital Ecosystem was then measured experimentally through simulations, with measures originating from theoretical ecology, evaluating its likeness to biological ecosystems. This included its responsiveness to requests for applications from the user base, as a measure of the ecological succession (ecosystem maturity). Overall, we have advanced the understanding of Digital Ecosystems, creating Ecosystem-Oriented Architectures where the word ecosystem is more than just a metaphor.Comment: 39 pages, 26 figures, journa

Evolution from the ground up with Amee – From basic concepts to explorative modeling

Evolutionary theory has been the foundation of biological research for about a century now, yet over the past few decades, new discoveries and theoretical advances have rapidly transformed our understanding of the evolutionary process. Foremost among them are evolutionary developmental biology, epigenetic inheritance, and various forms of evolu- tionarily relevant phenotypic plasticity, as well as cultural evolution, which ultimately led to the conceptualization of an extended evolutionary synthesis. Starting from abstract principles rooted in complexity theory, this thesis aims to provide a unified conceptual understanding of any kind of evolution, biological or otherwise. This is used in the second part to develop Amee, an agent-based model that unifies development, niche construction, and phenotypic plasticity with natural selection based on a simulated ecology. Amee is implemented in Utopia, which allows performant, integrated implementation and simulation of arbitrary agent-based models. A phenomenological overview over Amee’s capabilities is provided, ranging from the evolution of ecospecies down to the evolution of metabolic networks and up to beyond-species-level biological organization, all of which emerges autonomously from the basic dynamics. The interaction of development, plasticity, and niche construction has been investigated, and it has been shown that while expected natural phenomena can, in principle, arise, the accessible simulation time and system size are too small to produce natural evo-devo phenomena and –structures. Amee thus can be used to simulate the evolution of a wide variety of processes

A probabilistic cooperative-competitive hierarchical search model.

by Wong Yin Bun, Terence.Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.Includes bibliographical references (leaves 99-104).Abstract also in Chinese.List of Figures --- p.ixList of Tables --- p.xiChapter I --- Preliminary --- p.1Chapter 1 --- Introduction --- p.2Chapter 1.1 --- Thesis themes --- p.4Chapter 1.1.1 --- Dynamical view of landscape --- p.4Chapter 1.1.2 --- Bottom-up self-feedback algorithm with memory --- p.4Chapter 1.1.3 --- Cooperation and competition --- p.5Chapter 1.1.4 --- Contributions to genetic algorithms --- p.5Chapter 1.2 --- Thesis outline --- p.5Chapter 1.3 --- Contribution at a glance --- p.6Chapter 1.3.1 --- Problem --- p.6Chapter 1.3.2 --- Approach --- p.7Chapter 1.3.3 --- Contributions --- p.7Chapter 2 --- Background --- p.8Chapter 2.1 --- Iterative stochastic searching algorithms --- p.8Chapter 2.1.1 --- The algorithm --- p.8Chapter 2.1.2 --- Stochasticity --- p.10Chapter 2.2 --- Fitness landscapes and its relation to neighborhood --- p.12Chapter 2.2.1 --- Direct searching --- p.12Chapter 2.2.2 --- Exploration and exploitation --- p.12Chapter 2.2.3 --- Fitness landscapes --- p.13Chapter 2.2.4 --- Neighborhood --- p.16Chapter 2.3 --- Species formation methods --- p.17Chapter 2.3.1 --- Crowding methods --- p.17Chapter 2.3.2 --- Deterministic crowding --- p.18Chapter 2.3.3 --- Sharing method --- p.18Chapter 2.3.4 --- Dynamic niching --- p.19Chapter 2.4 --- Summary --- p.21Chapter II --- Probabilistic Binary Hierarchical Search --- p.22Chapter 3 --- The basic algorithm --- p.23Chapter 3.1 --- Introduction --- p.23Chapter 3.2 --- Search space reduction with binary hierarchy --- p.25Chapter 3.3 --- Search space modeling --- p.26Chapter 3.4 --- The information processing cycle --- p.29Chapter 3.4.1 --- Local searching agents --- p.29Chapter 3.4.2 --- Global environment --- p.30Chapter 3.4.3 --- Cooperative refinement and feedback --- p.33Chapter 3.5 --- Enhancement features --- p.34Chapter 3.5.1 --- Fitness scaling --- p.34Chapter 3.5.2 --- Elitism --- p.35Chapter 3.6 --- Illustration of the algorithm behavior --- p.36Chapter 3.6.1 --- Test problem --- p.36Chapter 3.6.2 --- Performance study --- p.38Chapter 3.6.3 --- Benchmark tests --- p.45Chapter 3.7 --- Discussion and analysis --- p.45Chapter 3.7.1 --- Hierarchy of partitions --- p.45Chapter 3.7.2 --- Availability of global information --- p.47Chapter 3.7.3 --- Adaptation --- p.47Chapter 3.8 --- Summary --- p.48Chapter III --- Cooperation and Competition --- p.50Chapter 4 --- High-dimensionality --- p.51Chapter 4.1 --- Introduction --- p.51Chapter 4.1.1 --- The challenge of high-dimensionality --- p.51Chapter 4.1.2 --- Cooperation - A solution to high-dimensionality --- p.52Chapter 4.2 --- Probabilistic Cooperative Binary Hierarchical Search --- p.52Chapter 4.2.1 --- Decoupling --- p.52Chapter 4.2.2 --- Cooperative fitness --- p.53Chapter 4.2.3 --- The cooperative model --- p.54Chapter 4.3 --- Empirical performance study --- p.56Chapter 4.3.1 --- pBHS versus pcBHS --- p.56Chapter 4.3.2 --- Scaling behavior of pcBHS --- p.60Chapter 4.3.3 --- Benchmark test --- p.62Chapter 4.4 --- Summary --- p.63Chapter 5 --- Deception --- p.65Chapter 5.1 --- Introduction --- p.65Chapter 5.1.1 --- The challenge of deceptiveness --- p.65Chapter 5.1.2 --- Competition: A solution to deception --- p.67Chapter 5.2 --- Probabilistic cooperative-competitive binary hierarchical search --- p.67Chapter 5.2.1 --- Overview --- p.68Chapter 5.2.2 --- The cooperative-competitive model --- p.68Chapter 5.3 --- Empirical performance study --- p.70Chapter 5.3.1 --- Goldberg's deceptive function --- p.70Chapter 5.3.2 --- "Shekel family - S5, S7, and S10" --- p.73Chapter 5.4 --- Summary --- p.74Chapter IV --- Finale --- p.78Chapter 6 --- A new genetic operator --- p.79Chapter 6.1 --- Introduction --- p.79Chapter 6.2 --- Variants of the integration --- p.80Chapter 6.2.1 --- Fixed-fraction-of-all --- p.83Chapter 6.2.2 --- Fixed-fraction-of-best --- p.83Chapter 6.2.3 --- Best-from-both --- p.84Chapter 6.3 --- Empricial performance study --- p.84Chapter 6.4 --- Summary --- p.88Chapter 7 --- Conclusion and Future work --- p.89Chapter A --- The pBHS Algorithm --- p.91Chapter A.1 --- Overview --- p.91Chapter A.2 --- Details --- p.91Chapter B --- Test problems --- p.96Bibliography --- p.9

A Single-Cell Taxonomy Predicts Inflammatory Niche Remodeling to Drive Tissue Failure and Outcome in Human AML

Cancer initiation is orchestrated by an interplay between tumor-initiating cells and their stromal/immune environment. Here, by adapted single-cell RNA sequencing, we decipher the predicted signaling between tissue-resident hematopoietic stem/progenitor cells (HSPC) and their neoplastic counterparts with their native niches in the human bone marrow. LEPR + stromal cells are identified as central regulators of hematopoiesis through predicted interactions with all cells in the marrow. Inflammatory niche remodeling and the resulting deprivation of critical HSPC regulatory factors are predicted to repress high-output hematopoietic stem cell subsets in NPM1-mutated acute myeloid leukemia (AML), with relative resistance of clonal cells. Stromal gene signatures reflective of niche remodeling are associated with reduced relapse rates and favorable outcomes after chemotherapy across all genetic risk categories. Elucidation of the intercellular signaling defining human AML, thus, predicts that inflammatory remodeling of stem cell niches drives tissue repression and clonal selection but may pose a vulnerability for relapse-initiating cells in the context of chemotherapeutic treatment.</p

STABLE ADAPTIVE STRATEGY of HOMO SAPIENS and EVOLUTIONARY RISK of HIGH TECH. Transdisciplinary essay

The co-evolutionary concept of Three-modal stable evolutionary strategy of Homo sapiens is developed. The concept based on the principle of evolutionary complementarity of anthropogenesis: value of evolutionary risk and evolutionary path of human evolution are defined by descriptive (evolutionary efficiency) and creative-teleological (evolutionary correctly) parameters simultaneously, that cannot be instrumental reduced to others ones. Resulting volume of both parameters define the trends of biological, social, cultural and techno-rationalistic human evolution by two gear mechanism ˗ gene-cultural co-evolution and techno- humanitarian balance. The resultant each of them can estimated by the ratio of socio-psychological predispositions of humanization/dehumanization in mentality. Explanatory model and methodology of evaluation of creatively teleological evolutionary risk component of NBIC technological complex is proposed. Integral part of the model is evolutionary semantics (time-varying semantic code, the compliance of the biological, socio-cultural and techno-rationalist adaptive modules of human stable evolutionary strategy)