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Introduction
Started with Computer Algorithms in high school, then with Visual Programming, then with Genetic Programming, then with Optical Computing, and now with robots.
Additional affiliations
March 2017 - February 2020
January 2007 - February 2017
January 2004 - January 2007
Publications
Publications (108)
Finding the optimal parameter setting (i.e. the optimal population size, the optimal mutation probability, the optimal evolutionary
model etc) for an Evolutionary Algorithm (EA) is a difficult task. Instead of evolving only the parameters of the algorithm
we will evolve an entire EA capable of solving a particular problem. For this purpose the Mult...
Multi Expression Programming (MEP) is a new evolutionary paradigm intended for solving computationally difficult problems. MEP individuals are linear entities that encode complex computer programs. MEP chromosomes are represented in the same way as C or Pascal compilers translate mathematical expressions into
machine code. MEP is used for solving s...
In this paper we suggest the use of light for performing useful computations. Namely, we propose a special device which uses light rays for solving the Hamiltonian path problem on a directed graph. The device has a graph-like representation and the light is traversing it following the routes given by the connections between nodes. In each node the...
Determining the author of a text is a difficult task. Here we compare multiple AI techniques for classifying literary texts written by multiple authors by taking into account a limited number of speech parts (prepositions, adverbs, and conjunctions). We also introduce a new dataset composed of texts written in the Romanian language on which we have...
Determining the author of a text is a difficult task. Here, we compare multiple Artificial Intelligence techniques for classifying literary texts written by multiple authors by taking into account a limited number of speech parts (prepositions, adverbs, and conjunctions). We also introduce a new dataset composed of texts written in the Romanian lan...
Multi Expression Programming (MEP) is a Genetic Programming variant which encodes multiple solutions in a single chromosome. This paper introduces and deeply describes several strategies for solving binary and multi-class classification problems within the multi solutions per chromosome paradigm of MEP. Extensive experiments on various classificati...
We investigate the possibility of encoding multiple solutions of a problem in a single chromosome. The best solution encoded in an individual will represent (will provide the fitness of) that individual. In order to obtain some benefits the chromosome decoding process must have the same complexity as in the case of a single solution in a chromosome...
A new model for evolving Evolutionary Algorithms (EAs) is proposed in this paper. The model is based on the Multi Expression Programming (MEP) technique. Each MEP chromosome encodes an evolutionary pattern that is repeatedly used for generating the individuals of a new generation. The evolved pattern is embedded into a standard evolutionary scheme...
Traceless Genetic Programming (TGP) is a Genetic Programming (GP) variant that is used in cases where the focus is rather the output of the program than the program itself. The main difference between TGP and other GP techniques is that TGP does not explicitly store the evolved computer programs. Two genetic operators are used in conjunction with T...
Traceless Genetic Programming (TGP) is a new Genetic Programming (GP) that may be used for solving difficult real-world problems. The main difference between TGP and other GP techniques is that TGP does not explicitly store the evolved computer programs. In this paper, TGP is used for solving real-world classification problems taken from PROBEN1. N...
A genetic programming (GP) variant called traceless genetic programming (TGP) is proposed in this paper. TGP is a hybrid method combining a technique for building individuals and a technique for representing individuals. The main difference between TGP and other GP techniques is that TGP does not explicitly store the evolved computer programs. Two...
Automatic Programming is one of the most important areas of computer science research today. Hardware speed and capability have increased exponentially, but the software is years behind. The demand for software has also increased significantly, but it is still written in old fashion: by using humans. There are multiple problems when the work is don...
Fruits-360 is a database containing images of fruits, vegetables, nuts, and seeds. Here, an improved version of it is introduced. The improvements are focused on the following directions: (1) adding new information about objects, (2) adding new objects with new characteristics (like having diseases and in various stages of growth), and (3) enhancin...
Multi Expression Programming (MEP) is a Genetic Programming variant that uses a linear representation of chromosomes. MEP individuals are strings of genes encoding complex computer programs.
When MEP individuals encode expressions, their representation is similar to the way in which compilers translate C or Pascal expressions into machine code.
A u...
Multi Expression Programming (MEP) is a new evolutionary paradigm intended for solving computationally difficult problems. MEP individuals are linear entities that encode complex computer programs. MEP chromosomes are represented in the same way as C or Pascal compilers translate mathematical expressions into machine code. MEP is used for solving s...
Finding the optimal parameter setting (i.e. the optimal population size, the optimal mutation probability, the optimal evolutionary model etc) for an Evolutionary Algorithm (EA) is a difficult task. Instead of evolving only the parameters of the algorithm we will evolve an entire EA capable of solving a particular problem. For this purpose the Mult...
Multi Expression Programming (MEP) is a Genetic Programming variant that uses linear chromosomes for solution encoding. A unique feature of MEP is its ability of encoding multiple solutions of a problem in a single chromosome. In this paper we use Multi Expression Programming for evolving digital circuits for a well-known NP-Complete problem: the k...
A new model for evolving Evolutionary Algorithms is proposed in this paper. The model is based on the Linear Genetic Programming (LGP) technique. Every LGP chromosome encodes an EA which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization, the Traveling Salesman Problem, and the Quadratic Assignment P...
An evolutionary approach for computing the winning strategy for Nim-like games is proposed in this paper. The winning strategy is computed by using the Multi Expression Programming (MEP) technique - a fast and efficient variant of the Genetic Programming (GP). Each play strategy is represented by a mathematical expression that contains mathematical...
According to the No Free Lunch (NFL) theorems all black-box algorithms perform equally well when compared over the entire set of optimization problems. An important problem related to NFL is finding a test problem for which a given algorithm is better than another given algorithm. Of high interest is finding a function for which Random Search is be...
Reversible computing basically means computation with less or not at all electrical power. Since the standard binary gates are not usually reversible we use the Fredkin gate in order to achieve reversibility. An algorithm for designing reversible digital circuits is described in this paper. The algorithm is based on Multi Expression Programming (ME...
Jenny 5 is a fully open-source robot intended to be used mainly for research but can also act as a human assistant. It has a mobile platform with rubber tracks, a flexible leg, two arms with 7 degrees of freedom each and head with 2 degrees of freedom. The robot is actuated by 20 motors (DC, steppers and servos) and its state is read with the help...
In this paper we introduce a new, high-quality, dataset of images containing fruits. We also present the results of some numerical experiment for training a neural network to detect fruits. We discuss the reason why we chose to use fruits in this project by proposing a few applications that could use this kind of neural network.
In this paper we introduce a new, high-quality, dataset of images containing fruits. We also present the results of some numerical experiment for training a neural network to detect fruits. We discuss the reason why we chose to use fruits in this project by proposing a few applications that could use this kind of neural network.
We describe here an optical device, based on time-delays, for solving the set splitting problem which is well-known NP-complete problem. The device has a graph-like structure and the light is traversing it from a start node to a destination node. All possible (potential) paths in the graph are generated and at the destination we will check which on...
We describe here an optical device, based on time-delays, for solving the set splitting problem which is well-known NP-complete problem. The device has a graph-like structure and the light is traversing it from a start node to a destination node. All possible (potential) paths in the graph are generated and at the destination we will check which on...
Always check for updates at:
https://github.com/fruits-360
or
https://www.kaggle.com/moltean/fruits
The data-set used consists of 90380 images of fruits, vegetables and nuts spread across 103 labels.
The following fruits, vegetables and nuts and are included: Apples (different varieties: Crimson Snow, Golden, Golden-Red, Granny Smith, Pink Lad...
A C++ implementation for the Multiple Expression Programming, which is a Genetic programming variant with linear encoding of chromosomes. This example be used for solving regression and classification problems. Latest version can be obtained from https://github.com/mepx/mep-basic-src
A C++ implementation for Multiple Expression Programming technique, which is a Genetic programming variant with linear encoding of chromosomes. This example shows how to work with multiple subpopulations which are useful for maintaining the diversity of the population. Can be used for solving regression and classification problems. Latest version c...
This book constitutes the thoroughly refereed post-conference proceedings of the 4th International Workshop on Optical SuperComputing, OSC 2012, held in Bertinoro, Italy, in July 2012. The 11 papers presented together with 11 invited papers were carefully reviewed and selected for inclusion in this book. Being an annual forum for research presentat...
Optical computers use photons rather than electrons to represent and modify information. Computer architectures that are based on optics offer several interesting features: • Current architectures use energy to move electrons while photons move by nature. Thus, an optical information processing devices, consisting of passive components, may not use...
Since computer processing mainly depends on sorting and searching methods, a key problem is how to design efficient algorithms
in order to solve such problems. This paper describes a new nature-inspired mechanism (called Friction-based Sorting) capable
of sorting a given set of numbers. The main idea behind this mechanism is to associate a ball (wh...
This book constitutes the thoroughly refereed post-conference proceedings of the Third International Workshop on Optical SuperComputing, OSC 2010, held in Bertinoro, Italy, in November 2010.
The 13 papers presented were carefully reviewed and selected for inclusion in this book. Being an annual forum for research presentations on all facets of opti...
We study computational properties of Gheorge P¿un's P-systems extended with rules that model in an abstract way creation, dissolution, fusion and cloning of membranes. We investigate decision problems like reachability of a configuration, boundedness ...
We describe a delay-based optical device for solving the the Satisfiability problem. The device has a graph structure which is traversed by light in order to generate a solution. The device has 2 special nodes: a start node (where the initial pulse is sent) and a destination node (where the solution is read). Multiple signals are expected at the de...
We describe a self-repairing robotic car capable of changing its wheels automatically. The robot is constructed from a Lego NXT kit and a Lynx Arm kit which are coordinated from a PC. The difficulty consists in assembling the wheel on its shaft with a high precision which is not possible with the Lego components. This was solved by creating an ense...
Designing optical devices for solving NP-complete problems is a difficult task. The difficulty consists in constructing a graph which - when traversed by light - generates all possible solutions of the problem to be solved. So far only few devices of this type have been proposed. Here we suggest the use of evolutionary algorithms for solving this p...
Manual design of Evolutionary Algorithms (EAs) capable of performing very well on a wide range of problems is a difficult task. This is why we have to find other manners to construct algorithms that perform very well on some problems. One possibility (which is explored in this paper) is to let the evolution discover the optimal structure and parame...
A special computational device which uses light rays in order to decide whether there is a solution for the unbounded subset-sum problem is described in this paper. The device has a multigraph-like representation and the light traverses it follow-ing the routes given by the connections between the nodes. The graph has 3 nodes: the first one is wher...
We propose an optical computational device which uses light rays for solving the subset-sum problem. The device has a graph-like representation and the light is traversing it by following the routes given by the connections between nodes. The nodes are connected by arcs in a special way which lets us to generate all possible subsets of the given se...
Physics imposes some limits to the computations that we can perform. Because of these limits we cannot solve problems in almost no time and with almost zero energy consumption. The main problem is that we are asking too much from a single device: we want computers that play chess, solve equations, navigate on the internet etc. Because of this great...
Genetic Programming (GP) is an automated method for creating computer programs starting from a high-level description of the problem to be solved. Many variants of GP have been proposed in the recent years. In this paper we are reviewing the main GP variants with linear representation. Namely, Linear Genetic Programming, Gene Expression Programming...
Determining whether a Diophantine equation has a solution or not is the most important challenge in solving this type of problems. In this paper a special computational device which uses light rays is proposed to answer this question, namely check the existence of nonnegative solutions for linear Diophantine equations. The way of representation for...
String matching is a very important problem in computer science. The problem consists in flnding all the occurrences of a pattern P of length m in a text T of length n. We describe a special device which can do string matching by performing n¡m+1 text-to-pattern compar- isons. The proposed device uses light and optical fllters for performing comput...
The aim of this research is to develop an autonomous system for solving data analysis problems. The system, called Genetic Programming-Autonomous Solver (GP-AS) contains most of the features required by an autonomous software: it decides if it knows or not how to solve a particular problem, it can construct solutions for new problems, it can store...
The aim of this research is to develop an adaptive system for designing digital circuits. The investigated system, called
Adaptive Genetic Programming (AdGP) contains most of the features required by an adaptive GP algorithm: it can decide the
chromosome depth, the population size and the nodes of the GP tree which are the best suitable to provide...
We suggest a new optical solution for solving the YES/NO version of the Exact Cover problem by using the massive parallelism of light. The idea is to build an optical device which can generate all possible solutions of the problem and then to pick the correct one. In our case the device has a graph-like representation and the light is traversing it...
Evolutionary algorithms (EAs) can be used in order to design particle swarm optimization (PSO) algorithms that work, in some cases, considerably better than the human-designed ones. By analyzing the evolutionary process of designing PSO algorithms, we can identify different swarm phenomena (such as patterns or rules) that can give us deep insights...
In this paper we summarize the existing principles for building unconventional computing devices that involve delayed signals for encoding solutions to NP-complete problems. We are interested in the following aspects: the properties of the signal, the operations performed within the devices, some components required for the physical implementation,...
An intelligent system should be able to solve a wide range of problems from different domains. In this paper we propose a complex and adaptive system capable of solving various data analysis problems without needing human help for parameter settings. The system, called A-Brain, consists of several interconnected components (a decision-maker, a trai...
In this paper we propose a special computational device which uses light rays for solving the Hamiltonian path problem on a directed graph. The device has a graph-like representation and the light is traversing it by following the routes given by the connections between nodes. In each node the rays are uniquely marked so that they can be easily ide...
In spite of evolution of electronic techniques, a large number of applications continue to rely on the use of paper as the dominant medium. Bank checks are a widely known example. When filled by hand, the processing of the written information requires either a human or a special software which has intelligent abilities. This paper examines the issu...
Best subtree genetic programming (BSTGP) is a special genetic programming (GP) variant whose aim is to offer more possibilities, for selecting the solution, compared to standard GP. In the case of BSTGP the best subtree is chosen for proving the solution. This is different from standard GP where the solution was given by the entire tree. In this pa...
Traceless genetic programming (TGP) is a genetic programming (GP) variant that is used in cases where the focus is on the output of the program rather than the program itself. The main difference between TGP and other GP techniques is that TGP does not explicitly store the evolved computer programs. Two genetic operators are used in conjunction wit...
Evolutionary Algorithms (EAs) can be used for designing Particle Swarm Optimization (PSO) algorithms that work, in some cases, considerably better than the human-designed ones. By analyzing the evolutionary process of design PSO algorithm we can identify different swarm phenomena (such as patterns or rules) that can give us deep insights about the...
Classical kernel-based classifiers only use a single kernel, but the real-world applications have emphasized the need to con- sider a combination of kernels - also known as a multiple kernel - in order to boost the performance. Our purpose is to automatically find the mathematical expression of a mul- tiple kernel by evolutionary means. In order to...
The result of the program encoded into a Genetic Program- ming (GP) tree is usually returned by the root of that tree. However, this is not a general strategy. In this paper we present and investigate a new variant where the best sub- tree is chosen to provide the solution of the problem. The other nodes (not belonging to the best subtree) are dele...
A new model for automatic generation of Evolutionary Algorithms (EAs) by evolutionary means is proposed in this paper. The model is based on a simple Genetic Algorithm (GA). Every GA chromosome encodes an EA, which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization are evolved by using the considered...
Standard kernel-based classifiers use only a single kernel, but the real-world applications and the recent developments of
various kernel methods have emphasized the need to consider a combination of multiple kernels. We propose an evolutionary
approach for finding the optimal weights of a combined kernel used by the Support Vector Machines (SVM) a...
A new Genetic Programming variant called Liquid State Ge- netic Programming (LSGP) is proposed in this paper. LSGP is a hybrid method combining a dynamic memory for storing the inputs (the liquid) and a Genetic Programming technique used for the problem solving part. Several numerical experiments with LSGP are performed by using sev- eral benchmark...
A complex model for evolving the update strategy of a Particle Swarm Optimisation (PSO) algorithm is described in this paper. The model is a hybrid technique that combines a Genetic Algorithm (GA) and a PSO algorithm. Each GA chromosome is an array encoding a meaning for updating the particles of the PSO algorithm. The Evolved PSO algorithm is comp...
A new model for evolving Evolutionary Algorithms (EAs) is proposed in this paper. The model is based on the Multi Expression Programming (MEP) technique. Each MEP chromosome encodes an evolutionary pattern which is repeatedly used for generating the individuals of a new generation. The evolved pattern is embedded into a standard evolutionary scheme...
The possibility of using switchable glass (also called smart windows) technology for Evolvable Hardware tasks is suggested in this paper. Switchable glass technology basically means controlling the transmission of light through windows by using electrical power. By applying a variable voltage to the window we can continuously vary the amount of tra...
Multi Expression Programming is a Genetic Programming variant that uses a linear representation of individuals. A unique feature of Multi Expression Programming is its ability of storing multiple solutions of a problem in a single chromosome. In this paper, we propose and use several techniques for improving the search performed by Multi Expression...
Multi Expression Programming (MEP) is a Genetic Programming variant that uses a linear representation of chromosomes. MEP individuals are strings of genes encoding complex computer programs. When MEP individuals encode expressions, their representation is similar to the way in which compilers translate C or Pascal expressions into machine code.
A u...
Intelligence is strongly related to the ability of solving different problems by a single system. General problems solvers such as Artificial Neural Networks, Evolutionary Algorithms, Particle Swarm etc, have traditionally been tested against one problem at one time. The purpose of this research is to build a complex and adaptive system able to sol...
A new model for evolving the structure of a Particle Swarm Optimization (PSO) algorithm is proposed in this paper. The model is a hybrid technique that combines a Genetic Algorithm (GA) and a PSO algorithm. Each GA chromosome is an array encoding a meaning for updating the particles of the PSO algorithm. The evolved PSO algo- rithm is compared to a...
A new model for evolving crossover operators for evolutionary function optimization is proposed in this paper. The model is a hybrid technique that combines a Genetic Programming (GP) algorithm and a Genetic Algorithm (GA). Each GP chromosome is a tree encoding a crossover operator used for function optimization. The evolved crossover is embedded i...
In this chapter we propose a new evolutionary elitist approach combining a non-standard solution representation and an evolutionary optimization technique. The proposed method permits detection of continuous decision regions. In our approach an individual (a solution) is either a closed interval or a point. The individuals in the final population g...
A new technique called Adaptive Representation Evolutionary Algorithm (AREA) is proposed in this paper. AREA involves dynamic alphabets for encoding solutions. The proposed adaptive representation is more compact than binary representation. Genetic operators are usually more aggressive when higher alphabets are used. Therefore the proposed encoding...
Reversible computing basically means computation with less or not at all electrical power. Since the standard binary gates are not usually reversible we use the Fredkin gate in order to achieve reversibil- ity. An algorithm for designing reversible digital circuits is described in this paper. The algorithm is based on Multi Expression Programming (...
A new model for evolving Evolutionary Algorithms is proposed in this paper. The model is based on the Linear Genetic Programming (LGP) technique. Every LGP chromosome encodes an EA which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization, the Traveling Salesman Problem and the Quadratic Assignment Pr...
This paper proposes a novel adaptive representation for evolutionary multiobjective optimization for solving a stock modeling problem. The standard Pareto achieved evolution strategy (PAES) uses real or binary representation for encoding solutions. Adaptive Pareto archived evolution strategy (APAES) uses dynamic alphabets for encoding solutions. AP...
We investigate the possibility of encoding multiple solutions of a problem in a single chromosome. The best solution encoded in an individual will represent (will provide the fitness of) that individual. In order to obtain some benefits the chromosome decoding process must have the same complexity as in the case of a single solution in a chromosome...
Automatic Programming is one of the most important areas of computer science research today. Hardware speed and capability has increased exponentially, but the software is years behind. The demand for software has also increased significantly, but the it is still written in old-fashion: by using humans.
There are multiple problems when the work is...
Multi expression programming (MEP) is a genetic programming (GP) variant that uses linear chromosomes for solution encoding. A unique MEP feature is its ability of encoding multiple solutions of a problem in a single chromosome. These solutions are handled in the same time complexity as other techniques that encode a single solution in a chromosome...