All are approaches that exploits the computational intelligence paradigm. Machine learning is refered to data analitics. Evolutionary computation deal with optimization problems.

All are approaches that exploits the computational intelligence paradigm. Machine learning is refered to data analitics. Evolutionary computation deal with optimization problems.

In brief, AI is an umbrella, ML is a part of AI, and generic algorithm is a particular algorithm used in AI/ML.

Definition of AI

“It is the study of how to train the computers so that computers can do things which at present human can do better.”

AI is decision making.

AI leads to develop a system to mimic human to respond behave in a circumstances.

AI will go for finding the optimal solution.

Definition of ML

“Machine learning algorithms can figure out how to perform important tasks by generalizing from examples.”

ML allows system to learn new things from data.

ML allows involves in creating self learning algorithms.

ML will go for only solution for that whether it is optimal or not.

ML is used for pattern recognition.

Evolutionary Algorithms.

Evolutionary methods are optimization problems.

ML & EA are ways of solving problems.

AI is the comprehensive, ML is a part of AI, and generic algorithm/ evolutionary algorithms is (are) algorithms used in AI/ML for optimization problems.

I think that most of existing machine learning algorithms can not be viewed as AI. To compare with machine learning and Evolutionary algorithms, the former aims to learn an model which approximates unknown distributions such as datasets. The latter aims to search an optimal solution for the known optimization problem which has explicit forms.

AI is the overall discipline to cover several subdisciplins like computer vision, language understanding and translation, even object oriented programming was in the early stage of AI was included. Other areas coverd a voice recognition, machine learning, expert systems, business intelligence or rule based systems, ATMS (assumtion based truth maintenance systems like in KEE), genetic programming, fuzzy logic based expert systems or decision support systems, decision trees, etc.

The most powerfull AI systems are hybrid systems that combine different technologies and algorithms to solve a certain problem. Neural networks are the traditional machine learning algorithms, as the computing power of modern CPUs or even specialized CPUs or GPUs or APUs allow an extremly fast evalution making them even usefull in cheap consumer products (like digital cameras, headphones, etc). Specialized AI processors integrated in many smartphones today typically are very fast parallel math units supports ML or even DL to make them usefull in realtime applications.

Regarding literature you will find tons of valid literature. I dont think a single one is outdated, the algorithms from the early AI are still valid and I would look for

https://www.amazon.de/Innovation-Genetic-Algorithms-Evolutionary-Computation/dp/1475736452/ref=sr_1_1?__mk_de_DE=%C3%85M%C3%85%C5%BD%C3%95%C3%91&keywords=goldberg+evolutionary&qid=1555491915&s=books-intl-de&sr=1-1-catcorr

https://www.amazon.de/Readings-Machine-Learning-Morgan-Kaufmann/dp/1558601430/ref=sr_1_5?__mk_de_DE=%C3%85M%C3%85%C5%BD%C3%95%C3%91&keywords=readings+in+machine+learning&qid=1555492005&s=books-intl-de&sr=1-5

https://www.amazon.de/Deep-Learning-Definitive-Practitioners-Approach/dp/1491914254/ref=sr_1_3?__mk_de_DE=%C3%85M%C3%85%C5%BD%C3%95%C3%91&crid=1IARWND2MVUG0&keywords=deep+learning+adaptive+computation+and+machine+learning+series&qid=1555492063&s=books-intl-de&sprefix=deep+learning%2Cenglish-books%2C167&sr=1-3-catcorr

Just my 5 cents. ;-)

Good luck.

1) Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction. Particular applications of AI include expert systems, speech recognition and machine vision.

2) Machine learning is a subset of AI. That is, all machine learning counts as AI, but not all AI counts as machine learning. For example, symbolic logic – rules engines, expert systems and knowledge graphs – could all be described as AI, and none of them are machine learning.

One aspect that separates machine learning from the knowledge graphs and expert systems is its ability to modify itself when exposed to more data; i.e. machine learning is dynamic and does not require human intervention to make certain changes. That makes it less brittle, and less reliant on human experts.

A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. –Tom Mitchell

3) Genetic Algorithm (GA, part of Evolutionary algorithm) based on Biological Survival of Species. It iterates random objects starting from generation 1 till n. Each generation, object with the highest probability to survive yields children. The metrics to measure survivality is called fitness of the object. Till n generation, the traits of the highest fitness of the objects of each generation are carried forward.

Science Digest. 1981. The quantum monkey. Vol.8: 57.

breifly, you can consult this document: http://ceur-ws.org/Vol-2351/invited_paper_1.pdf

Dear Colleagues and Friends from RG, The relationship between artificial intelligence, machine learning and genetic / evolutionary algorithms consists of various types of relationships and relationships that can be briefly characterized as follows:

1. The concept of artificial intelligence in many popular scientific texts is abused because most of what is currently developing in various fields of industry and services, including information services offered via the Internet, e.g. chatbot technology and avatars simulating people advisers is not in the full sense of the word artificial intelligence only technology learning the machine also included among the key elements of the current fourth technological revolution Industry 4.0. On the other hand, currently developing machine learning technologies by continuing technological progress in subsequent years can be developed to the level of algorithmic data processing that can be determined by artificial intelligence.

2. Improving algorithms in developed technologies, learning machines can lead to the emergence of advanced artificial intelligence in the future, which will be used in many industries and services. The improvement of algorithms in developed technologies, learning machines can also lead to the approach of these increasingly advanced systems and algorithmic activities to what is called genetic / evolutionary algorithms, i.e. algorithms known from living organisms. Currently, the degree of organization of biological systems, the degree of organization of the central nervous system is considered so complex that for many years man-made artificial systems of neural networks may not match those of biological. However, despite a much lower complexity, genetic algorithms can carry out a comparable number of data processing operations. Currently built quantum computers are capable of performing many times more and faster data processing operations compared to biological neural networks in the central nervous system with a lower level of complexity.

3. Artificial neural networks improved and developed in subsequent generations built in the formula of modeling on biological networks of genetic algorithms support the process of technology development learning machines. In the perspective of the next few years, the technological progress implemented in this way may lead to the emergence of advanced artificial intelligence. However, it will be "only" artificial intelligence devoid of awareness of its existence and feelings. It will not be possible to create artificial emotional intelligence with self-awareness. It will be possible to create autonomous androids that can be equipped with artificial intelligence with programmed algorithms of certain behaviors imitating emotional reactions and simulating thinking organisms, but they will not be equipped with consciousness that people have. Considerations remain currently functioning and presented as possible in the near future in science fiction novels and movies (e.g. in the film "Transcendence", in which the main role of the scientist's hero was played by Johnny Deep) solutions consisting of uploading human memories, memory resources, knowledge and awareness with elements of feelings for artificial intelligence installed in a supercomputer with high computing power and large disk capacity. In the next few years, many current questions about the possibilities of developing learning machines, creating new generations of artificial intelligence and its applications in various spheres, branches of the economy should be answered.

Best wishes

Dariusz Prokopowicz