Child Drawing Development Optimization Algorithm based on Child’s Cognitive Development
TL;DR: A novel metaheuristic Child Drawing Development Optimization algorithm inspired by the child's learning behaviour and cognitive development using the golden ratio to optimize the beauty behind their art and reveals the competency of the algorithm to evade local minima.
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Abstract: This paper proposes a novel metaheuristic Child Drawing Development Optimization (CDDO) algorithm inspired by the child's learning behavior and cognitive development using the golden ratio to optimize the beauty behind their art. The golden ratio was first introduced by the famous mathematician Fibonacci. The ratio of two consecutive numbers in the Fibonacci sequence is similar, and it is called the golden ratio, which is prevalent in nature, art, architecture, and design. CDDO uses golden ratio and mimics cognitive learning and child's drawing development stages starting from the scribbling stage to the advanced pattern-based stage. Hand pressure width, length and golden ratio of the child's drawing are tuned to attain better results. This helps children with evolving, improving their intelligence and collectively achieving shared goals. CDDO shows superior performance in finding the global optimum solution for the optimization problems tested by 19 benchmark functions. Its results are evaluated against more than one state-of-art algorithms such as PSO, DE, WOA, GSA, and FEP. The performance of the CDDO is assessed, and the test result shows that CDDO is relatively competitive through scoring 2.8 ranks. This displays that the CDDO is outstandingly robust in exploring a new solution. Also, it reveals the competency of the algorithm to evade local minima as it covers promising regions extensively within the design space and exploits the best solution.
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References
Theories and Activities of Conceptual Artists: An Aesthetic Inquiry
Luise Morton
- 01 Jan 1983
Abstract: Of the various art movements which emerged in the late 60s and early 70s, Conceptual ism is among the most radical in its attack on traditional theories of art. As a movement, Conceptual ism has been syncretic, comprising many groups and individuals with different, often conflicting, theories and practices. The movement has been international in scope, and artists in Europe and America have contributed various interpretations of and emphases to the postulates of Conceptual ism. In general, however, Conceptual artists have centered on two questions: "What is the nature of art?" and "What is the function or usefulness of art?" The purpose of my research is an aesthetic inquiry into the theories and art work of Conceptual artists.
3
•Book
Early Childhood Development: A Multicultural Perspective
Jeffrey W. Trawick-Smith
- 02 Oct 1996
TL;DR: Theories of early childhood development have been surveyed in this paper, including cognitive development in infants, physical growth and motor development in children, and social and emotional development in the primary years.
•Book
Children's Cognitive Development and Learning
Usha Goswami
- 01 Dec 2007
TL;DR: The infant brain has a number of powerful learning mechanisms at its disposal, even prior to birth as discussed by the authors, and the foetus can hear through the amniotic fluid during the third trimester, and memory for the mother's voice is developed while the baby is in the womb.
The golden ratio.
TL;DR: In the article on perceived aesthetics of maxillary incisors1 no mention was made of the Golden Ratio, a ratio 1.61:1 which occurs in nature and science and has been used in architecture; reputedly the Parthenon was built to these proportions.
Socio-inspired Optimization Metaheuristics: A Review
Meeta Kumar,Anand J. Kulkarni,Anand J. Kulkarni +2 more
- 01 Jan 2019
TL;DR: The chapter attempts to review the recent literature in the upcoming area of socio-inspired metaheuristics, a novel subbranch of the popular Evolutionary algorithms under the class of nature-inspired algorithms for optimization.

![Figure 3:Random scribble [32]](/figures/figure-3-random-scribble-32-2bs16wrn.png)

![Figure 4: Controlled scribbles [32]](/figures/figure-4-controlled-scribbles-32-3og4bvvo.png)
![Figure 5: Head and Feet Symbols [32]](/figures/figure-5-head-and-feet-symbols-32-ur2otubo.png)
