scholarly journals Human-Competitive Machine Intelligence by Means of Genetic Algorithms

2005 ◽  
Author(s):  
John R. Koza
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Genetic Programming ◽  
Computer Programs ◽  
Electrical Circuit ◽  
Machine Intelligence ◽  
Wide Range ◽  
Placement And Routing ◽  
High Level ◽  
The Given

The subtitle of John Holland's pioneering 1975 book Adaptation in Natural and Artificial Systems correctly anticipated that the genetic algorithm described in that book would have "applications to.. .artificial intelligence." When the entities in the evolving population are computer programs, Holland's genetic algorithm can be used to perform the task of searching the space of computer programs for a program that solves, or approximately solves, a problem. This variation of the genetic algorithm (called genetic programming) enables the genetic algorithm to address the long-standing challenge of getting a computer to solve a problem without explicitly programming it. Specifically, this challenge calls for an automatic system whose input is a high-level statement of a problem's requirements and whose output is a satisfactory solution to the given problem. Paraphrasing Arthur Samuel [33], this challenge concerns "How can computers be made to do what needs to be done, without being told exactly how to do it?" This challenge is the common goal of such fields of research as artificial intelligence and machine learning. Arthur Samuel [32] offered one measure for success in this pursuit, namely "The aim [is].. .to get machines to exhibit behavior, which if done by humans, would be assumed to involve the use of intelligence." Since a problem can generally be recast as a search for a computer program, genetic programming can potentially solve a wide range of problems, including problems of control, classification, system identification, and design. Section 2 describes genetic programming. Section 3 states what we mean when we say that an automatically created solution to a problem is competitive with the product of human creativity. Section 4 discusses the illustrative problem of automatically synthesizing both the topology and sizing for an analog electrical circuit. Section 5 discusses the problem of automatically determining the placement and routing (while simultaneously synthesizing the topology and sizing) of an electrical circuit. Section 6 discusses the problem of automatically synthesizing both the topology and tuning for a controller. Section 7 discusses the importance of illogic in achieving creativity and inventiveness.

scholarly journals Propane Pre-Reforming into Methane-Rich Gas over Ni Catalyst: Experiment and Kinetics Elucidation via Genetic Algorithm

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3393 ◽  
Author(s):  
Sergey I. Uskov ◽  
Dmitriy I. Potemkin ◽  
Leniza V. Enikeeva ◽  
Pavel V. Snytnikov ◽  
Irek M. Gubaydullin ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Molar Ratio ◽  
Ni Catalyst ◽  
Propane Conversion ◽  
Reaction Conditions ◽  
Experimental Conditions ◽  
Chromium Catalyst ◽  
Nickel Chromium ◽  
Wide Range ◽  
The Given

Pre-reforming of propane was studied over an industrial nickel-chromium catalyst under pressures of 1 and 5 bar, at a low steam to carbon molar ratio of 1, in the temperature range of 220–380 °C and at flow rates of 4000 and 12,000 h−1. It was shown that propane conversion proceeded more efficiently at low pressure (1 atm) and temperatures above 350 °C. A genetic algorithm was applied to search for kinetic parameters better fitting experimental results in such a wide range of experimental conditions. Power law and Langmuir–Hinshelwood kinetics were considered. It was shown that only Langmuir–Hinshelwood type kinetics correctly described the experimental data and could be used to simulate the process of propane pre-reforming and predict propane conversion under the given reaction conditions. The significance of Langmuir–Hinshelwood kinetics increases under high pressure and temperatures below 350 °C.

scholarly journals A Survey on Artificial Intelligence Overview

2020 ◽  
Author(s):  
Aggarwal AJuhi ◽  
Shailesh Kumar
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Expert System ◽  
Pattern Matching ◽  
Machine Intelligence ◽  
Human Beings ◽  
Matching Method ◽  
System P ◽  
Wide Range ◽  
Computational Relationship

Artificial Intelligence (AI) is the branch of computer science concerned with the study and creation of computer of computer system are more intelligence than human. Artificial Intelligence programmed by the human beings. We can increase the AI’s capabilities by the supervised and unsupervised teaching. Artificial Intelligence works with pattern matching method which attempts to describe objects, events or process in terms of their qualitative features, logical and computational relationship. AI can also be used to make predications in future. Artificial Intelligence helps people to make their tasks easily and efficiently. Intelligence is the way of thinking and acting upon the environment, this might depend upon the the programming. There is huge difference on the Natural Intelligence (NI), Machine Intelligence (MI) and Artificial Intelligence. There is wide range of application for that ranges from computer vision to expert system.

scholarly journals Implementation of integrated design space exploration of scheduling, allocation and binding in high level synthesis using multi structure genetic algorithm

2021 ◽  
Author(s):  
Michael Gebremariam
Keyword(s):  
Genetic Algorithm ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Integrated Design ◽  
High Level Synthesis ◽  
C Language ◽  
Low Level ◽  
Wide Range ◽  
High Level

The objective of this project is to develop a software tool which assists in comparison of a work known as "M-GenESys: Multi Structure Genetic Algorithm based Design Space Exploration System for Integrated Scheduling, Allocation and Binding in High Level Synthesis" with another well established GA approach known as "A Generic Algorithm for the Design Space Exploration of Data paths During High-Level Synthesis". Two sets of software are developed based on both approaches using Microsoft Visual 2005 C# language. The C# language is an object-oriented language that is aimed at enabling programmers to quickly develop a wide range of applications on the Microsoft .NET platform. The goal of C# and the .NET platform is to shorten development time by freeing the developer from worrying about several low level plumbing issues such as memory equipment, type safety issues, building low level libraries, array bound checking, etc., thus allowing developers to actually spend their time and energy working on the application and business logic.

scholarly journals GPU implementation of restricted Boltzmann machines with application to view classification in sports videos

2021 ◽  
Author(s):  
Andreas Fred Bernitzke
Keyword(s):  
Genetic Algorithm ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
High Level Synthesis ◽  
Restricted Boltzmann Machines ◽  
C Language ◽  
Low Level ◽  
Wide Range ◽  
High Level

The objective of this project is to develop a software tool which assists in comparison of a work known as "M-GenESys: Multi Structure Genetic Algorithm based Design Space Exploration System for Integrated Scheduling, Allocation and Binding in High Level Synthesis" with another well established GA approach known as "A Genetic Algorithm for the Design Space Exploration of Data paths During High-Level Synthesis". Two sets of Software are developed based on both approaches using Microsoft visual 2005,C# language. The C# language is an object-oriented language that is aimed at enabling programmers to quickly develop a wide range of applications on the Microsoft .NET platform. The goal of C# and the .NET platform is to shorten development time by freeing the developer from worrying about several low level plumbing issues such as memory management, type safety issues, building low level libraries, array bounds checking, etc. thus allowing developers to actually spend their time and energy working on the application and business logic.

scholarly journals APPLICATION OF GENETIC PROGRAMMING TOOLS AS A MEANS OF SOLVING OPTIMIZATION PROBLEMS

2018 ◽  
Vol 6 (52) ◽  
pp. 58-60
Author(s):  
A. Hafiiak ◽  
E. Borodina ◽  
A. Diachenko-Bohun
Keyword(s):  
Genetic Algorithm ◽  
Genetic Programming ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Electrical Circuit ◽  
Command Language ◽  
Software Products ◽  
Software Applications ◽  
Programming Tools ◽  
Software Implementations

Purpose. The article is devoted to the problem of practical application of genetic programming tools as a means of solving optimization problems and the use of genetic programming in various fields of activity. It is established that the evolution of genetic programming is directly related to the development of the genetic algorithm, it is also determined that with the passage of time a significant improvement in genetic programming has occurred. Since the advent of the genetic algorithm, many modifications and software implementations have appeared. This in turn led to the implementation of the genetic algorithm toolkit in software products, namely: specialized software, applications for mathematical and analytical packages, frameworks and libraries. The article reveals the significant impact of genetic programming in the areas of: quantum computing, electrical circuit design, etc. Not only advantages, but also disadvantages are considered, attention is also paid to methods of eliminating deficiencies by improving optimization methods and applying a genetic algorithm. Results. The analysis of the main directions of the practical use of genetic programming is carried out and tasks that can be effectively solved using this toolkit are outlined. Scientific novelty. It was determined that the improvement of optimization methods and the expansion of the use of genetic algorithms, stimulates the appearance of such software products on the market, simplifies the structure of software tools, designs the interface for working with a specific commercial user community, simplifies the command language, which allows the use of genetic programming tools circle of users with different levels of training.

scholarly journals Implementation of integrated design space exploration of scheduling, allocation and binding in high level synthesis using multi structure genetic algorithm

2021 ◽  
Author(s):  
Michael Gebremariam
Keyword(s):  
Genetic Algorithm ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Integrated Design ◽  
High Level Synthesis ◽  
C Language ◽  
Low Level ◽  
Wide Range ◽  
High Level

The objective of this project is to develop a software tool which assists in comparison of a work known as "M-GenESys: Multi Structure Genetic Algorithm based Design Space Exploration System for Integrated Scheduling, Allocation and Binding in High Level Synthesis" with another well established GA approach known as "A Generic Algorithm for the Design Space Exploration of Data paths During High-Level Synthesis". Two sets of software are developed based on both approaches using Microsoft Visual 2005 C# language. The C# language is an object-oriented language that is aimed at enabling programmers to quickly develop a wide range of applications on the Microsoft .NET platform. The goal of C# and the .NET platform is to shorten development time by freeing the developer from worrying about several low level plumbing issues such as memory equipment, type safety issues, building low level libraries, array bound checking, etc., thus allowing developers to actually spend their time and energy working on the application and business logic.

On Abstract Intelligence

2012 ◽  
pp. 14-31
Author(s):  
Yingxu Wang
Keyword(s):  
Artificial Intelligence ◽  
Formal Model ◽  
Machine Intelligence ◽  
Human Intelligence ◽  
Engineering Applications ◽  
Measurement Framework ◽  
Wide Range ◽  
Intelligence Theories ◽  
The Brain ◽  
Intelligent Quotient

Abstract intelligence is a human enquiry of both natural and artificial intelligence at the reductive embodying levels of neural, cognitive, functional, and logical from the bottom up. This paper describes the taxonomy and nature of intelligence. It analyzes roles of information in the evolution of human intelligence, and the needs for logical abstraction in modeling the brain and natural intelligence. A formal model of intelligence is developed known as the Generic Abstract Intelligence Mode (GAIM), which provides a foundation to explain the mechanisms of advanced natural intelligence such as thinking, learning, and inferences. A measurement framework of intelligent capability of humans and systems is comparatively studied in the forms of intelligent quotient, intelligent equivalence, and intelligent metrics. On the basis of the GAIM model and the abstract intelligence theories, the compatibility of natural and machine intelligence is revealed in order to investigate into a wide range of paradigms of abstract intelligence such as natural, artificial, machinable intelligence, and their engineering applications.

scholarly journals GPU implementation of restricted Boltzmann machines with application to view classification in sports videos

2021 ◽  
Author(s):  
Andreas Fred Bernitzke
Keyword(s):  
Genetic Algorithm ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
High Level Synthesis ◽  
Restricted Boltzmann Machines ◽  
C Language ◽  
Low Level ◽  
Wide Range ◽  
High Level

The objective of this project is to develop a software tool which assists in comparison of a work known as "M-GenESys: Multi Structure Genetic Algorithm based Design Space Exploration System for Integrated Scheduling, Allocation and Binding in High Level Synthesis" with another well established GA approach known as "A Genetic Algorithm for the Design Space Exploration of Data paths During High-Level Synthesis". Two sets of Software are developed based on both approaches using Microsoft visual 2005,C# language. The C# language is an object-oriented language that is aimed at enabling programmers to quickly develop a wide range of applications on the Microsoft .NET platform. The goal of C# and the .NET platform is to shorten development time by freeing the developer from worrying about several low level plumbing issues such as memory management, type safety issues, building low level libraries, array bounds checking, etc. thus allowing developers to actually spend their time and energy working on the application and business logic.

Genetic Programming for Automatically Constructing Data Mining Algorithms

2011 ◽  
pp. 932-936 ◽  
Author(s):  
Alex A. Freitas ◽  
Gisele L. Pappa
Keyword(s):  
Data Mining ◽  
Natural Selection ◽  
Genetic Programming ◽  
Search Algorithm ◽  
Computer Programs ◽  
Current Data ◽  
Data Mining Algorithms ◽  
Alternative Approach ◽  
Wide Range ◽  
Mining Algorithms

At present there is a wide range of data mining algorithms available to researchers and practitioners (Witten & Frank, 2005; Tan et al., 2006). Despite the great diversity of these algorithms, virtually all of them share one feature: they have been manually designed. As a result, current data mining algorithms in general incorporate human biases and preconceptions in their designs. This article proposes an alternative approach to the design of data mining algorithms, namely the automatic creation of data mining algorithms by means of Genetic Programming (GP) (Pappa & Freitas, 2006). In essence, GP is a type of Evolutionary Algorithm – i.e., a search algorithm inspired by the Darwinian process of natural selection – that evolves computer programs or executable structures. This approach opens new avenues for research, providing the means to design novel data mining algorithms that are less limited by human biases and preconceptions, and so offer the potential to discover new kinds of patterns (or knowledge) to the user. It also offers an interesting opportunity for the automatic creation of data mining algorithms tailored to the data being mined.

TOWARDS ADVANCED DEVELOPMENT OF CYBORG INTELLIGENCE

2018 ◽  
Vol 23 (3) ◽  
pp. 201-211
Author(s):  
Dewi Agushinta R ◽  
Fiena Rindani ◽  
Antonius Angga Kurniawan ◽  
Elevanita Anggari ◽  
Rizky Akbar
Keyword(s):  
Artificial Intelligence ◽  
Cognitive Architecture ◽  
Sensory Information ◽  
Specific Area ◽  
Machine Intelligence ◽  
Scientific Model ◽  
Neural Signals ◽  
Wide Range ◽  
Robot Tasks ◽  
Intelligence Development

The creation of machines with human intelligence is an primary and beneficial aim of artificial intelligence research. One interesting method in developing artificial intelligence is combining a biological method and machine intelligence. Cyborg Intelligence is a new scientific model for the integration of biological and machinery. Brain Machine Interface (BMI) provides an opportunity to integrate both intelligence at various levels. Based on BMI, neural signals can be read for the control of motor actuators and sensory information coding machine can be sent to a specific area of the brain. In fact, Distributed Adaptive Control Theory of Mind and Brain technology is the most advanced brain-based cognitive architecture successfully applied in a wide range of robot tasks. It is expected that by analyzing the cyborg intelligence development can help and facilitate to enhance the knowledge of cyborg intelligence.

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