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are not new in the study of social insects . What is new is the explicit recognition, within the biologists mainstream, that they may hold one important key to help fully understand eusociality. In fact, the awareness of the other (empathy) has been proposed to be one of the traits helping organisms to cross the barrie... |
. In humans this would be accomplished by language; in insects by chemical, tactile and visual communication than enhance their ability to interact hence forming cohesive grouping. Interactivity, in fact, seems to be a primary trait underlying grouping in social insects. Depending on the intensity of one-to-one interac... |
4.2 Non-randomness and interaction dynamics Random mutations are at the centre of current evolutionary paradigm. While it is true that bacteria, for example, adapt and develop resistance to almost every antibiotic that is developed, not a single new species has been observed to arise after decades (hence, thousands or ... |
Are random mutations a real mechanism for genetic variation and evolutionary change or are they part of a limited working hypothesis that must me revisited and complemented with, for example, mutationless evolution |
or evolution by means of horizontal recombination mechanisms ? An illustrative example regarding random mechanism in theoretical ecology is useful at this point. For years it was thought that random climatic variations were responsible for driving population dynamics. However after the pioneering work of Robert May and... |
, it became clear that variations in population numbers may be due to the intrinsic changes of the ecosystems and the non-linear universe of interactions on it. These erratic fluctuations are not random but chaotic and the difference between these concepts is not trivial. One is the outcome of stochastic casino-like ev... |
4.3 Mobility: come and stay together Another common widespread idea in biology is that individual social and ecological interactions follow essentially random patterns. Take for example mobility and dispersal. Since the 70s of the last century, it became theoretically obvious that a simplified agent would explore its s... |
There is growing evidence that biological organisms perform anomalous diffusion in their mobility patterns in the form of Lévy flights (scale-free probability distributions in the lengths of travelled distances). When efficient social interactions occur in the context of density-dependent ecosystems then another intere... |
Cyanobacteria, as said before, are intriguing social organisms that have been protagonists of important evolutionary changes in the history of life. Despite its apparent simplicity, they are known to have very complex patterns of non-random mobility, cell-to-cell interactions and communication |
. Cyanobacteria do form pairwise ensembles of interaction and mobility. It would not be surprising at all that their mobility patterns are anomalous diffusion and so their social engagements may respond to optimized encounter rates. It will be also very interesting to know how and when these patterns have emerged in th... |
4.4 Stay together then interact Another front that must be included in a more comprehensive evolutionary theory of cooperation is the evolution of social interactions. It has been clear since the last two decades that social interactions obey a scale-free network pattern and that it seems to be ubiquitous in nature. Ge... |
It was shown in models of ant-to-ant interactions, that a colony is posed at an order-disorder phase transition where sociality emerges and information capacity is at its best (Figure ). In models of spider monkey foraging, it was found that the individual interactions with a given forest structure pose the ecosystem i... |
A novel theory of social evolution must integrate the concepts of the science of Complex Systems with those of the Darwinian tradition. Gene-centric concepts should be reviewed and complemented with evidence from multilevel phenomena (group selection), the constrains given by the non-linear nature of biological dynamic... |
Acknowledgements We thank the late Dr. A. Chopps for many years of convivial and inspiring discussion, and Dr. Karo Michaelian, Dr. Alessandra Marins and Dr. Paulo Cristaldo who kindly reviewed previous versions of this text providing invaluable insights. OM thanks DGAPA-PAPIIT Grant IN101712 and the Brazilian Ciência ... |
# Source: arxiv 1404.7765 # Title: A semantic network-based evolutionary algorithm for computational creativity # Sections: all # Downloaded: 2026-03-03T01:57:51.974535+00:00 |
A semantic network-based evolutionary algorithm for computational creativity (Received: date / Accepted: date) Abstract We introduce a novel evolutionary algorithm (EA) with a semantic network-based representation. For enabling this, we establish new formulations of EA variation operators, crossover and mutation, that ... |
Keywords: Evolutionary computation Memetic algorithms Memetics Analogical reasoning Semantic networks Introduction We introduce an evolutionary algorithm (EA) that generates semantic networks under a fitness measure based on information content and structure. This algorithm is, to the best of our knowledge, the first i... |
The algorithm works by fitness-based selection and reproduction of networks undergoing gradual changes introduced by variation operators. The initial generation of networks, and the variation operators of mutation and crossover, make use of randomly picked concepts and relations that are associated with existing nodes ... |
We demonstrate the approach via a fitness function measuring analogical similarity to a given base network. This is particularly interesting from an analogical reasoning perspective, because it enables us to spontaneously generate analogical mappings and novel analogous cases, in contrast with existing algorithms capab... |
Seeing the evolutionary optimization of information represented within semantic networks as an implementation of the idea of “memes” in cultural evolution, this algorithm can be interpreted as a novel type of memetic algorithm (MA). In this designation, we use the term “memetic” in a different technical sense from exis... |
This is due to several reasons. Within the existing field of MA, one models the effects of cultural evolution as a local refinement process for each individual, running on top of a global, population-based, optimization (Moscato et al., 2004 . So, the emphasis is on the local refinement of each individual due to memeti... |
In contrast, the emphasis in our approach is directly on the memetic evolution itself, given 1. it is the units of information (represented as semantic networks) that are undergoing variation, reproduction, and selection, exactly as in the original metaphor by Dawkins ( 1976 |
2. we have variation operators developed specific for this knowledge representation-based approach, respecting the semantics and commonsensical correctness of the evolving structures; and |
3. the whole process is guided by a fitness measure that is defined as a function of some selected set of features of the knowledge represented by each individual. |
The article is organized as follows. In Sect. we provide background information on the subjects of evolution, creativity, and culture, followed by a brief review existing models of graph-based EA, to enable a discussion of how our contribution is related with existing work in the field. In Sect. , we go over a detailed... |
Background 2.1 Evolution, creativity, and culture Following the success and explanatory power of evolutionary theory in biology, insights about the ubiquity of evolutionary phenomena have paved the way towards an understanding that these processes are not necessarily confined to biology. That is to say, whenever one ha... |
Within this larger framework, the concept of meme first introduced as a metaphor by Dawkins ( 1976 as an evolving unit of culture analogous to a gene , hosted, altered, and reproduced in minds, later formed the basis of the approach called memetics |
Popularized by Hofstadter and Dennett ( 1981 , the explanation found itself use in cultural and sociological studies. For example, Balkin ( 1998 argues that ideologies can be explained using a meme-based description, produced through processes of cultural evolution and transcending the lives of individuals. This evolut... |
The first point of view basically discusses the role of arts and creativity in the general framework of classical evolutionary biology, considering the provided advantages for adaptation and survival. All known societies enjoy creative pursuits such as literature, music, and visual arts; and there is evidence from the ... |
Alternatively, inspired by the insight that evolutionary processes are not confined to biology, and using evolutionary theories of sociocultural change, one can consider that culture itself is possibly recreated through evolutionary processes occurring in the abstract environment of thoughts, concepts, or ideas. An exa... |
Surely, a unifying approach considering all types of evolution is also possible, studying it as a general phenomenon applying at different levels to both physical and cultural systems. Within the creativity field, this kind of approach is taken by Skusa and Bedau ( 2002 , who study the processes occurring in systems ex... |
A similar dichotomy also exists in the interpretation of the role of evolutionary algorithms in computational creativity. Researchers realize that EA can be applied to computational creativity problems, considering them as a new area of complex and difficult technical problems where they can employ the proven power of ... |
Again, as in the case of sociocultural evolution, one can also consider the creativity process itself as taking place through evolutionary processes in an abstract “creativity space”. In cases where evidence for an underlying evolutionary process can be spotted (as in the case of cultural evolution), in addition to pro... |
The work that we present in this article is open to both interpretations. In addition to being a technique for the generation of semantic networks for a given creativity task, we can also use it—due to its memetic interpretation—to model the evolution of human culture through passing generations. |
2.2 Graph-based evolutionary algorithms There are several existing algorithms using graph-based representations for the encoding of candidate solutions in EA Montes and Wyatt ( 2004 . The most notable work among these is genetic programming (GP) Koza et al. ( 2003 , where candidate solutions are pieces of computer prog... |
In parallel distributed genetic programming (PDGP) Poli ( 1999 , the restrictions of the tree structure of GP are relaxed by allowing multiple outputs from a node, which allows a high degree of parallelism in the evolved programs. In evolutionary graph generation (EGG) Chen et al. ( 2002 the focus is on evolving graphs... |
The use of a graph-based representation makes the design of variation operators specific to graphs necessary. In works such as GNP, this is facilitated by using a string-based encoding of node names, types, and connectivity, permitting operators very close to their counterparts in conventional EA; and in PDGP, the oper... |
Our approach in this article, on the other hand, is closely related with how GP handles variation. In GP crossover operation, two candidate solutions are combined to form two new solutions as their offspring. This is accomplished by randomly selecting crossover fragments in both parents, deleting the selected fragment ... |
In GP, there are two main types of mutations: the first one involves the random change of the type of a function or terminal at a randomly selected position in the candidate solution; while in the second one an entire subtree of the candidate solution can be replaced by a new randomly created subtree. |
What is common within GP related algorithms is that the output of each node in the graph can constitute an input to another node. In comparison, for the semantic network-based representation that we will introduce, the range of connections that can form a graph of a given set of concepts is constrained by commonsense k... |
Of the existing graph-based EAs, the implementation nicknamed McGonagall by Manurung ( 2003 bears similarities to our approach in that it uses a “flat semantic representation” that is essentially equivalent to what we here call semantic networks. McGonagall uses an EA approach to poetry generation, using fitness measur... |
The algorithm Our algorithm, outlined in Algorithm , proceeds similar to conventional EA, with a relatively small set of parameters. |
Algorithm 1 Procedure for the novel semantic network-based memetic algorithm. Refer to Table for an overview of involved parameters. |
1: procedure MemeticAlgorithm 2: [MATH] InitializePopulation [MATH] [MATH] [MATH] [MATH] 3: repeat 4: [MATH] EvaluateFitnesses [MATH] |
5: [MATH] NextGeneration [MATH] [MATH] [MATH] [MATH] [MATH] [MATH] [MATH] [MATH] [MATH] 6: [MATH] 7: [MATH] 8: until stop criterion |
9: end procedure Descriptions of initialization, fitness evaluation, selection, and memetic variation steps are presented in detail in the following sections. The parameters affecting each step of the algorithm, along with their explanations, are summarized in Table |
3.1 Semantic networks Semantic networks are graphs that represent semantic relations between concepts. In a semantic network, knowledge is expressed in the form of directed binary relations, represented by edges, and concepts, represented by nodes. This type of graph representation has found use in many subfields of ar... |
Figure shows a graph representation of a simple semantic network. In addition to the graphical representation, we also adopt the notation of IsA(bird, animal) to mean that the concepts bird and animal are connected by the directed relation IsA( [MATH] [MATH] , i.e. “bird is an animal”. |
An important characteristic of a semantic network is whether it is definitional or assertional: in definitional networks the emphasis is on taxonomic relations (e.g. [MATH] ) describing a subsumption hierarchy that is true by definition; in assertional networks, the relations describe instantiations and assertions that... |
3.2 Commonsense reasoning A foundational issue that comes with our approach is the problem of reconciling the intrinsically random nature of evolutionary operations with the requirement that the evolving semantic networks should be meaningful. |
This is so because, unlike existing graph-based approaches such as GP or GNP, not every node in a semantic network graph can be connected to an arbitrary other node through an arbitrary type of relation. This issue is relevant in every type of modification operation that needs to be executed during the course of our al... |
Simply put, the operations should be constrained by commonsense knowledge: a relation such as IsA(bird, animal) is meaningful, while Causes(bird, table) is not. |
We address this problem by utilizing the nascent subfield of AI named commonsense reasoning (Mueller, 2006 ; Havasi et al., 2007 . Within AI, since the pioneering work by McCarthy ( 1958 , commonsense reasoning has been commonly regarded as a key ability that a system must possess in order to be considered truly intell... |
Commonsense reasoning refers to the type of reasoning involved in everyday human thinking, based on commonsense knowledge that an ordinary person is expected to know, or “the knowledge of how the world works” (Mueller, 2006 . It comprises information such as HasA(human, brain) IsA(sun, star) , or CapableOf(ball, roll) ... |
Knowledge bases such as the Cyc project maintained by Cycorp company , ConceptNet project of MIT Media Lab , and the Never-Ending Language Learning (NELL) project of Carnegie Mellon University are set up to collect and classify commonsense information for the use of research community. In our current implementation, we... |
3.2.1 Knowledge bases The ConceptNet project is a part of the Open Mind Common Sense (OMCS) initiative of the MIT Media Lab, based on the input of commonsense knowledge from general public through several ways, including parsed natural language and semi-structured fill-in-the-blanks type forms (Havasi et al., 2007 . As... |
Access to the ConceptNet database is provided through a web API using JavaScript Object Notation (JSON) textual data format. Due to performance reasons, we use the previous version of ConceptNet, version 4, in our implementation. This is because of the high volume of queries to ConceptNet during the creation of random ... |
According to the study by Diochnos ( 2013 , ConceptNet version 4 includes 566,094 assertions and 321,993 concepts. The variety of assertions in ConceptNet, initially contributed by volunteers from general public, makes it somewhat prone to noise. According to our experience, noise is generally due to charged statements... |
The lexical database WordNet (Fellbaum, 1998 maintained by the Cognitive Science Laboratory at Princeton University also has characteristics of a commonsense knowledge base that make it attractive for our purposes. WordNet is based on a grouping of words into synsets or synonym rings which hold together all elements th... |
In addition to these synset groupings, WordNet includes pointers that are used to represent relations between the words in different synsets. These include semantic pointers that represent relations between word meanings and lexical pointers that represent relations between word forms. |
For treating WordNet as a commonsense knowledge base compatible with ConceptNet, we define the set of correspondences we outline in Table . Similar approaches have also been used by other researchers in the field, such as by Kuo and Hsu ( 2010 |
In the implementation of our algorithm, we answer the various types of queries to commonsense knowledge bases (such as the RandomConcept() call in Algorithm ) via ConceptNet or WordNet on a random basis. When the query is answered by information retrieved from WordNet, we return the information formatted in ConceptNet ... |
In our implementation we use WordNet version 3, contributing definitional relations involving around 117,000 synsets. Another thing to note here is that, in ConceptNet version 5, WordNet already constitutes one of the main incorporated data sources. This means that, in case we switch from ConceptNet version 4 to versio... |
3.3 Initialization At the start of a run, the population of size [MATH] is initialized (Algorithm ) with individuals created through a procedure that we call random semantic network generation (Algorithm ), capable of assembling random semantic networks of any given size. |
Figure presents an example of a random semantic network created via this procedure. This works by starting from a network comprising a sole concept randomly picked from commonsense knowledge bases and running a semantic network expansion algorithm that |
1. randomly picks a concept in the given network (e.g. human ); 2. compiles a list of relations, from commonsense knowledge bases, that the picked concept can be involved in (e.g. CapableOf(human, |
think) Desires(human, eat) , …); 3. appends to the network a relation randomly picked from this list, together with the other involved concept; and |
4. repeats this process until a given number of concepts have been appended to the network, or a set timeout [MATH] has been reached (as a failsafe for situations where there are not enough relations involving the concepts in the network being created). |
It is very important to note here that even if it is grown in a random manner, the generated network itself is totally meaningful, because it is a combination of meaningful pieces of information harvested from commonsense knowledge bases |
The initialization algorithm depends upon the parameters of [MATH] , the intended number of concepts in the randomly created semantic networks, and [MATH] , the minimum ConceptNet relation score that should be satisfied by the retrieved relations (Table ). |
Algorithm 2 Procedure for the creation of initial random population. 1: procedure InitializePopulation [MATH] [MATH] [MATH] [MATH] |
2: initialize [MATH] [MATH] The return array 3: for [MATH] times do 4: [MATH] RandomNetwork [MATH] [MATH] [MATH] [MATH] Generate a new random network |
5: AppendTo [MATH] [MATH] 6: end for 7: return [MATH] 8: end procedure Algorithm 3 The random semantic network generation algorithm. The algorithm is presented here in a form simpler than the actual implementation, for the sake of clarity. |
1: procedure RandomNetwork [MATH] [MATH] [MATH] 2: initialize [MATH] [MATH] Empty return network 3: initialize [MATH] [MATH] Random initial seed concept |
4: for [MATH] times do 5: [MATH] RandomConcept [MATH] 6: [MATH] InvolvedRelations [MATH] 7: if Size [MATH] [MATH] then 8: AppendTo [MATH] [MATH] |
9: break for [MATH] Favor a seed with more than a few relations 10: end if 11: end for 12: [MATH] 13: repeat 14: [MATH] RandomConceptIn [MATH] |
15: [MATH] InvolvedRelations [MATH] [MATH] The set of relations involving [MATH] 16: [MATH] RandomRelationIn [MATH] 17: if Score [MATH] [MATH] |
then 18: AppendTo [MATH] [MATH] [MATH] Append to the network [MATH] the relation [MATH] and its involved concepts 19: end if 20: |
[MATH] 21: until Size [MATH] [MATH] or [MATH] 22: return [MATH] 23: end procedure 3.4 Fitness measure After the initial generation is populated by individuals created by the random semantic network generation algorithm that we outlined, the algorithm proceeds by assigning fitness values to each individual. Since our ap... |
As an example for showcasing our approach, in Sect. , we define a fitness measure based on analogical similarity to an existing semantic network, giving rise to spontaneous generation of semantic networks that are in each generation more and more structurally analogous to a given network. |
In general terms, a direct and very interesting application of our approach would be to devise realistically formed fitness functions modeling selectionist theories of knowledge, which remain untested until this time. One such theory is the evolutionary epistemology theory of Campbell (Bickhard and Campbell, 2003 , whi... |
It is also possible to make the inclusion of certain concepts in the evolving semantic networks a requirement, allowing the discovery of networks formed around a given set of seed concepts. This can be also achieved through starting the initialization procedure (Algorithm ) with the given seed concepts. |
After all the individuals in the current generation are assigned fitness values, the algorithm proceeds with the creation of the next generation of individuals through variation operators (Algorithm ). But before this, the algorithm has to apply selection to pick individuals from the current population that will be “su... |
3.5 Selection After the assignment of fitness values, individuals are replaced with offspring generated via variation operators applied on selected parents. We employ tournament selection, because it is better at preserving population diversity and allowing selection pressure to be adjusted through simple parameters (P... |
Tournament selection involves, for each selection event, running “tournaments” among a group of [MATH] randomly selected individuals. Individuals in the tournament pool then challenge each other in pairs and the individual with the higher fitness will win with probability [MATH] . This method simulates biological matin... |
In our implementation, we also allow reselection, meaning that the same individual from a particular generation can be selected more than once to produce offspring in different combinations. Algorithm gives an overview of the selection procedure that we implement. |
Algorithm 4 Implemented selection algorithm. 1: procedure Select [MATH] [MATH] [MATH] [MATH] 2: [MATH] RandomMember [MATH] [MATH] Current winner |
3: for [MATH] times do 4: [MATH] RandomMember [MATH] [MATH] The next opponent 5: if LookupFitness [MATH] [MATH] [MATH] LookupFitness [MATH] [MATH] then |
6: if RandomReal (0, 1) [MATH] then 7: [MATH] [MATH] Opponent defeats current winner 8: end if 9: end if 10: end for 11: return [MATH] |
12: end procedure 3.6 Memetic variation operators Variation operators form the last step in the cycle of our algorithm by creating the next generation of individuals before going back to the step of fitness evaluation (Algorithm ). |
As we mentioned in Sect. 3.2 , our representation does not permit arbitrary connections between different nodes in the network and requires special variation operators that should respect the commonsense structure of represented knowledge. |
In the following sections, we present the commonsense crossover and commonsense mutation operators that we set up specific to semantic networks. |
Using these operators, the next step in the cycle of our algorithm is the creation of the offspring through variation (Algorithm ). Crossover is applied to parents selected from the population until [MATH] offspring are created (Table ), where each crossover event creates two offspring from two parents. |
Following the tradition in the GP field (Koza et al., 2003 , we design the variation process such that the offspring created by crossover do not undergo mutation. The mutation operator is applied only to the rest of individuals that are copied, or “reproduced”, directly from the previous generation. |
For generating the remaining part of the population, we reproduce [MATH] number of individuals selected, and make these subject to mutation. We employ elitism: the last individual (hence the remaining [MATH] in the previous equation) is a copy of the one with the current best fitness. |
Algorithm 5 Procedure for generating the next generation of individuals. 1: procedure NextGeneration [MATH] [MATH] [MATH] [MATH] [MATH] [MATH] [MATH] [MATH] [MATH] |
2: initialize [MATH] [MATH] The return array 3: [MATH] [MATH] Number of crossover events 4: [MATH] [MATH] Number of reproduction events |
5: for [MATH] times do 6: [MATH] Select [MATH] [MATH] [MATH] [MATH] 7: [MATH] Select [MATH] [MATH] [MATH] [MATH] 8: [MATH] Crossover [MATH] [MATH] [MATH] Crossover the two parents |
9: AppendTo [MATH] , o1) [MATH] Two offspring from each crossover 10: AppendTo [MATH] , o2) 11: end for 12: for [MATH] times do 13: |
[MATH] Select [MATH] [MATH] [MATH] [MATH] 14: [MATH] Mutate [MATH] [MATH] [MATH] Mutate an individual 15: AppendTo [MATH] , m) 16: |
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