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Proceedings Papers
. isal2019, ALIFE 2019: The 2019 Conference on Artificial Life388-395, (July 29–August 2, 2019) 10.1162/isal_a_00191
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We evolve floating point Sextic polynomial populations of genetic programming binary trees for up to a million generations. Programs with almost 400 000 000 instructions are created by crossover. To support unbounded Long-Term Evolution Experiment LTEE GP we use both SIMD parallel AVX 512 bit instructions and 48 threads to yield performance of up to 149 billion GP operations per second, 149 giga GPops, on a single Intel Xeon Gold 6126 2.60 GHz server.
Proceedings Papers
. isal2019, ALIFE 2019: The 2019 Conference on Artificial Life380-387, (July 29–August 2, 2019) 10.1162/isal_a_00190
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Any part of a genome, considered separately from the rest of the genome, evolves against a “virtual fitness landscape” that results when the rest of the genome is held constant. We show how analyzing a genome in this way can explain one form of progressively increasing evolvability. When one part of a genome is a vector of numbers (“knobs”) and the rest is a graph that determines the mapping from knobs to phenotype, the graph will respond to selective pressure to “acclivate” the virtual fitness function faced by the knobs—that is, to make it more hill-shaped. For as long as the knobs’ virtual fitness function provides opportunity for distorting it to make knob-turning mutations improve fitness, the graph experiences pressure to evolve those distortions as a side-effect of responding to its own virtual fitness function. As the knobs’ virtual fitness function grows more hill-shaped, the knobs track upward paths more easily and hence so does the genotype as a whole. A synergy develops between incremental exploration of phenotypes by knob-mutations and discontinuous exploration by graph-mutations. A favorable condition for this is a global fitness function that frequently varies, changing constants but leaving structural invariants unchanged. The graph then accumulates a memory of the invariants as revealed across many previous epochs, held in the form of bias limiting and directing future evolution.
Proceedings Papers
. isal2019, ALIFE 2019: The 2019 Conference on Artificial Life372-379, (July 29–August 2, 2019) 10.1162/isal_a_00189
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In a collaborative society, sharing information is advantageous for each individual as well as for the whole community. Maximizing the number of agent-to-agent interactions per time becomes an appealing behavior due to fast information spreading that maximizes the overall amount of shared information. However, if malicious agents are part of society, then the risk of interacting with one of them increases with an increasing number of interactions. In this paper, we investigate the roles of interaction rates and times (aka edge life) in artificial societies of simulated robot swarms. We adapt their social networks to form proper trust sub-networks and to contain attackers. Instead of sophisticated algorithms to build and administrate trust networks, we focus on simple control algorithms that locally adapt interaction times by changing only the robots’ motion patterns. We successfully validate these algorithms in collective decision-making showing improved time to convergence and energy-efficient motion patterns, besides impeding the spread of undesired opinions.
Proceedings Papers
. isal2019, ALIFE 2019: The 2019 Conference on Artificial Life365-371, (July 29–August 2, 2019) 10.1162/isal_a_00188
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In this paper, we wish to investigate the dynamics of information transfer in evolutionary dynamics. We use information theoretic tools to track how much information an evolving population has obtained and managed to retain about different environments that it is exposed to. By understanding the dynamics of information gain and loss in a static environment, we predict how that same evolutionary system would behave when the environment is fluctuating. Specifically, we anticipate a cross-over between the regime in which fluctuations improve the ability of the evolutionary system to capture environmental information and the regime in which the fluctuations inhibit it, governed by a cross-over in the timescales of information gain and decay.
Proceedings Papers
. isal2019, ALIFE 2019: The 2019 Conference on Artificial Life357-364, (July 29–August 2, 2019) 10.1162/isal_a_00187
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To evolve or not to evolve? That is the question: whether ‘tis nobler in the mind to suffer the slings and arrows of grey goo, or to deny evolution to a sea of self-replicators and by prevention control them? We have been developing a physical self-replicating machine concept for deployment on the Moon built from local resources on the Moon. Here, we are concerned with architectural issues - we specifically address the problem of uncontrolled replication. We propose a multitiered approach to prevent this: (i) denial of self-replication through the implementation of centralised mass manufacturing of replicators; (ii) denial of scarce sodium and chlorine from Earth acts as an Earth-controlled kill switch in preventing further replication; (iii) denial of centralised supplies of asteroidal metals (tungsten-nickel-cobalt-selenium) at the lunar south pole acts as a Moon-controlled kill switch; (iv) denial of online learning capacity through fixed neural weights; (v) denial of extended computing resources through the elimination of transmit communications between self-replicators; (vi) denial of evolutionary capacity by implementing error detection and correction (EDAC) coding. Two kill switches and EDAC provide the backbone to our approach that maintain self-replication capability.
Proceedings Papers
. isal2019, ALIFE 2019: The 2019 Conference on Artificial Life349-356, (July 29–August 2, 2019) 10.1162/isal_a_00186
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Progress in molecular genetics allowed taxonomists to better understand the relationships between species without the bias of morphological similarities. However, access to data from times past is limited to the fossil archives which, being far from complete, can only provide limited information. To address this problem through the field of Artificial Life, we devised a polyvalent sexual reproduction scheme and an automated phylogenetic tool capable of producing, from a stream of genomes, hierarchical species trees with relatively low memory footprint. We assert that these apparatus perform well under reasonable stress by embedding them into 2D simulations of unsupervised plant evolution in textbook cases of geographical speciation. After thousands of generations and millions of plants, the extracted phylogenetic data not only showed the expected results in terms of branching pattern (anagenesis, cladogenesis) but also exhibited complex interactions between species both in space and time.
Proceedings Papers
. isal2019, ALIFE 2019: The 2019 Conference on Artificial Life341-348, (July 29–August 2, 2019) 10.1162/isal_a_00185
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There has been a revival of the notion of habit in the embodied and situated cognitive sciences. A habit can be understood as ‘a self-sustaining pattern of sensorimotor coordination that is formed when the stability of a particular mode of sensorimotor engagement is dynamically coupled with the stability of the mechanisms generating it’ (Barandiaran, 2008, p. 281). This view has inspired models of biologically-inspired homeostatic agents capable of establishing their own habits (Di Paolo and Iizuka, 2008). Despite recent achievements in this field, there is little written about how social habits can be established from this modelling perspective. We hypothesize that, when the stability of internal behavioural mechanisms is coupled to the stability of a behaviour and other agents are present during this behaviour, a social interdependence of behaviour takes place: a social habit is established. We provide evidence for our hypothesis with an evolutionary robotics simulation model of homeostatic plasticity in a phototactic behaviour. Agents evolved to couple internal homeostasis to behavioural fitness display social interdependencies in their behaviour. The social habit of these agents was not interrupted when blindness to phototactic stimuli was introduced as long as social perception remained active. This did not happen when internal homeostasis was not coupled to the fitness of the agent. The results allow us to propose a possible conjecture about the character of social habits and to offer a potential theoretical framework to understand how habits develop from neurodynamics to the level of social interaction.
Proceedings Papers
. isal2019, ALIFE 2019: The 2019 Conference on Artificial Life414-415, (July 29–August 2, 2019) 10.1162/isal_a_00195
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We aimed to investigate the principle of emerging interactions between swarms using the functional differentiation theory of the brain. We propose a heterogeneous swarms model, where two swarms having different parameters evolve to maximize transfer entropy between them. In our simulation, we found the emergence of heterogeneous behavior among the swarms, and the appearance of several interaction patterns depending on the degree of the transfer entropy. Our results imply that the same principle of functional differentiation may underlie both the brain and swarms, leading to a novel design of brain-inspired swarm intelligence.
Proceedings Papers
. isal2019, ALIFE 2019: The 2019 Conference on Artificial Life412-413, (July 29–August 2, 2019) 10.1162/isal_a_00194
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We present a theoretical framework that mathematically formulates the evolutionary dynamics of organism-environment couplings using graph product multilayer networks, i.e., networks obtained by “multiplying” factor networks using some graph product operator. In this framework, one factor network represents different options of environments and their mutual physical reachability, and another factor network represents possible types of organisms and their mutual evolutionary reachability. The organism-environment coupling space is given by a Cartesian product of these two factor networks, and the nodes of the product network represent specific organism-environment combinations. We studied a simple evolutionary model using a reaction-diffusion equation on this organism-environment coupling space. We numerically calculated correlations between the inherent fitness of organisms and the actual average fitness obtained from the graph product-based evolutionary model, varying the spatial diffusion rate while keeping the type diffusion rate small. Results demonstrated that, when the spatial diffusion is sufficiently slow, the correlation between inherent and actual fitnesses drops significantly, where it is no longer valid to assume that fitness can be attributed only to organisms.
Proceedings Papers
. isal2019, ALIFE 2019: The 2019 Conference on Artificial Life404-411, (July 29–August 2, 2019) 10.1162/isal_a_00193
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The social brain hypothesis posits that the evolution of big brains (neural complexity) in groups of social organisms is the evolutionary result of cognitive challenges associated with various complex interactions and the need to process and solve complex social tasks. This study aims to investigate the environmental and evolutionary conditions under which neural complexity evolves without sacrificing collective behavioral efficacy. Using an evolutionary collective robotics system this research evaluates the impact of imposing a fitness cost on evolving increased neural complexity in robot groups that must operate (accomplish cooperative tasks) in environments of varying complexity. Results indicate that for all environments tested, imposing a cost on neural complexity induces the evolution of smaller neural controllers that are comparably effective to more complex controllers.
Proceedings Papers
. isal2019, ALIFE 2019: The 2019 Conference on Artificial Life396-403, (July 29–August 2, 2019) 10.1162/isal_a_00192
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This paper studies the effects of changing environments on the evolution of bodies and brains of modular robots. Our results indicate that environmental history has a long lasting impact on the evolved robot properties. We show that if the environment gradually changes from type A to type B, then the evolved morphological and behavioral properties are very different from those evolving in a type B environment directly. That is, we observe some sort of “genetic memory”. Furthermore, we show that gradually introducing a difficult environment helps to reach fitness levels that are higher than those obtained under those difficult conditions directly. Finally, we also demonstrate that robots evolved in gradually changing environments are more robust, i.e., exhibit a more stable performance under different conditions.