Algorithms (GAs) are adaptive heuristic search algorithm premised on the evolutionary ideas of natural selection and genetic. The basic concept of GAs is designed to simulate processes in natural system necessary for evolution, specifically those that follow the principles first laid down by Charles Darwin of survival of the fittest. As such they represent an intelligent exploitation of a random search within a defined search space to solve a problem.
GAs as a number of different things:
*GAs as problem solvers
*GAs as challenging technical puzzle
*GAs as basis for competent machine learning
*GAs as computational model of innovation and creativity
*GAs as computational model of other innovating systems
*GAs as guiding philosophy


GAs were introduced as a computational analogy of adaptive systems. They are modelled loosely on the principles of the evolution via natural selection, employing a population of individuals that undergo selection in the presence of variation-inducing operators such as mutation and recombination (crossover). A fitness function is used to evaluate individuals, and reproductive success varies with fitness.
The Algorithms
1. Randomly generate an initial population M(0)
2. Compute and save the fitness u(m) for each individual m in the current population M(t)
3. Define selection probabilities p(m) for each individual m in M(t) so that p(m) is proportional to u(m)
4. Generate M(t+1) by probabilistically selecting individuals from M(t) to produce offspring via genetic operators
5. Repeat step 2 until satisfying solution is obtained.
The paradigm of GAs descibed above is usually the one applied to solving most of the problems presented to GAs. Though it might not find the best solution. more often than not, it would come up with a partially optimal solution.
everyone can gain benefits from Algorithms, once he can encode solutions of a given problem to chromosomes in GA, and compare the relative performance (fitness) of solutions. An effective GA representation and meaningful fitness evaluation are the keys of the success in GA applications. The appeal of GAs comes from their simplicity and elegance as robust search algorithms as well as from their power to discover good solutions rapidly for difficult high-dimensional problems. GAs are useful and efficient when
**The search space is large, complex or poorly understood.
**Domain knowledge is scarce or expert knowledge is difficult to encode to narrow the search space.
**No mathematical analysis is available.
**Traditional search methods fail.
The advantage of the GA approach is the ease with which it can handle arbitrary kinds of constraints and objectives; all such things can be handled as weighted components of the fitness function, making it easy to adapt the GA scheduler to the particular requirements of a very wide range of possible overall objectives.
GAs have been used for problem-solving and for modelling. GAs are applied to many scientific, engineering problems, in business and entertainment, including:

Optimization: GAs have been used in a wide variety of optimisation tasks, including numerical optimisation, and combinatorial optimisation problems such as traveling salesman problem (TSP), circuit design, job shop scheduling and video & sound quality optimisation.

Automatic Programming: GAs have been used to evolve computer programs for specific tasks, and to design other computational structures, for example, cellular automata and sorting networks.

Machine and robot learning: GAs have been used for many machine- learning applications, including classificationa and prediction, and protein structure prediction. GAs have also been used to design neural networks, to evolve rules for learning classifier systems or symbolic production systems, and to design and control robots.

Economic models: GAs have been used to model processes of innovation, the development of bidding strategies, and the emergence of economic markets.

Immune system models: GAs have been used to model various aspects of the natural immune system, including somatic mutation during an individual's lifetime and the discovery of multi-gene families during evolutionary time.

Ecological models: GAs have been used to model ecological phenomena such as biological arms races, host-parasite co-evolutions, symbiosis and resource flow in ecologies.

Population genetics models: GAs have been used to study questions in population genetics, such as "under what conditions will a gene for recombination be evolutionarily viable?"

Interactions between evolution and learning: GAs have been used to study how individual learning and species evolution affect one another.

Models of social systems: GAs have been used to study evolutionary aspects of social systems, such as the evolution of cooperation , the evolution of communication, and trail-following behaviour in ants.
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Applications of Algorithms

GA on optimisation and planning: Travelling Salesman Problem
The TSP is interesting not only from a theoretical point of view, many practical applications can be modelled as a travelling salesman problem or as variants of it, for example, pen movement of a plotter, drilling of printed circuit boards (PCB), real-world routing of school buses, airlines, delivery trucks and postal carriers. Researchers have tracked TSPs to study biomolecular pathways, to route a computer networks' parallel processing, to advance cryptography, to determine the order of thousands of exposures needed in X-ray crystallography and to determine routes searching for forest fires (which is a multiple-salesman problem partitioned into single TSPs). Therefore, there is a tremendous need for algorithms.
In the last two decades an enormous progress has been made with respect to solving travelling salesman problems to optimality which, of course, is the ultimate goal of every researcher. One of landmarks in the search for optimal solutions is a 3038-city problem. This progress is only party due to the increasing hardware power of computers. Above all, it was made possible by the development of mathematical theory and of efficient algorithms. Here, the GA approach is discussed.
There are strong relations between the constraints of the problem, the representation adopted and the genetic operators that can be used with it. The goal of traveling Salesman Problem is to devise a travel plan (a tour) which minimises the total distance travelled. TSP is NP-hard (NP stands for non-deterministic polynomial time) - it is generally believed cannot be solved (exactly) in time polynomial. The TSP is constrained:

*The salesman can only be in a city at any time

*Cities have to be visited once and only once.
When GAs applied to very large problems, they fail in two aspects:
1. They scale rather poorly (in terms of time complexity) as the number of cities increases.
2. The solution quality degrades rapidly.

Failure of Standard Algorithm
To use a standard GA, the following problems have to be solved:

A binary representation for tours is found such that it can be easily translated into a chromosome.

An appropriate fitness function is designed, taking the constraints into account.

Non-permutation matrices represent unrealistic solutions, that is, the GA can generate some chromosomes that do not represent valid solutions. This happens:

*in the random initialisation step of the GA.

*as a result of genetic operators (mutation and crossover).
Thus, permutation matrices are used. Two tours including the same cities in the same order but with different starting points or different directions are represented by different matrices and hence by different chromosomes, for example:
tour (23541) = tour (12354)

An proper fitness function is obtained using penalty-function method to enforce the constraints.
However, the ordinary genetic operators generate too many invalid solutions, leading to poor results. Alternative solutions to TSP require new representations ( Position Dependent Representations) and new genetic operators.
GAs as guiding philosophy

Evolutionary Divide and Conquer (EDAC)
This approach, EDAC, has potential for any search problem in which knowledge of good solutions for subproblems can be exploited to improve the solution of the problem itself. The idea is to use the Algorithm to explore the space of problem subdivisions rather than the space of solutions themselves, and thus capitalise on the near linear scaling qualities generally inherent in the divide and conquer approach.
The basic mechanisms for dissecting a TSP into subproblems, solving the subproblems and then patching the subtours together to form a global tour, have been obtained from the cellular dissection algorithms of Richard Karp. Although solution quality tends to be rather poor, Karp`s algorithms possess an attractively simple geometrical approach to dissection, and offer reasonable guarantees of performance. Moreover, EDAC approach is intrinsically parallel.
The EDAC approach has lifted the application of GAs to TSP an order or magnitude larger in terms of problem sizes than permutation representations. Experimental results demonstrate the successful properties for EDAC on uniform random points and PCB problems in the range 500 - 5000 cities.
GA in Business and Their Supportive Role in Decision Making
Algorithms have been used to solve many different types of business problems in functional areas such as finance, marketing, information systems, and production/ operations. Within these functional areas, GAs have performed a variety of applications such as tactical asset allocation, job scheduling, machine-part grouping, and computer network design.


Finance Applications
Models for tactical asset allocation and international equity strategies have been improved with the use of GAs. They report an 82% improvement in cumulative portfolio value over a passive benchmark model and a 48% improvement over a non-GA model designed to improve over the passive benchmark.
algorithms are particularly well-suited for financial modelling applications for three reasons:
1. They are payoff driven. Payoffs can be improvements in predictive power or returns over a benchmark. There is an excellent match between the tool and the problems addressed.
2. They are inherently quantitative, and well-suited to parameter optimisation (unlike most symbolic machine learning techniques).
3. They are robust, allowing a wide variety of extensions and constraints that cannot be accommodated in traditional methods."

Information Systems Applications
Distributed computer network topologies are designed by a GA, using three different objective functions to optimise network reliability parameters, namely diameter, average distance, and computer network reliability. The GA has successfully designed networks with 100 order of nodes.
GA has also been used to determine file allocation for a distributed system. The objective is to maximise the programs' abilities to reference the file s located on remote nodes. The problem is solved with the following three different constraint sets:
1. There is exactly one copy of each file to be distributed.
2. There may be any number of copies of each file subject to a finite memory constraint at each node.
3. The number of copies and the amount of memory are both limited.


Production/Operation Applications
Algorithm has been used to schedule jobs in a sequence dependent setup environment for a minimal total tardiness. All jobs are scheduled on a single machine; each job has a processing time and a due date. The setup time of each job is dependent upon the job which immediately precedes it. The GA is able to find good, but not necessarily optimal schedules, fairly quickly.
GA is also used to schedule jobs in non-sequence dependent setup environment. The jobs are scheduled on one machine with the objective of minimising the total generally weighted penalty for earliness or tardiness from the jobs' due dates. However, this does not guarantee that it will generate optimal solutions for all schedules.
GA is developed for solving the machine-component grouping problem required for cellular manufacturing systems. GA provides a collection of satisfactory solutions for a two objective environment (minimising cell load variation and minimising volume of inter cell movement), allowing the decision maker to then select the best alternative.
Role in Decision Making
Applying the well established decision processing phase model of Simon (1960), Algorithms appear to be very well suited for supporting the design and choice phases of decision making.

*In solving a single objective problem, GA designs many solutions until no further improvement (no increase in fitness) can be achieved or some predetermined number of generations have evolved or when the allotted processing time is complete. The most fit solution in the final generation is the one that maximises or minimises the objective (fitness) function; this solution can be thought of as the GA has recommended choice. Therefore with single objective problems the user of GA is assisted in the choice phase of decision processing.

*When solving multi-objective problems, GA gives out many satisfactory solutions in terms of the objectives, and then allows the decision maker to select the best alternative. Therefore GAs assist with the design phase of decision processing with multi-objective problems.
GAs can be of great assistance for examining alternatives since they are designed to evaluate existing potential solutions as well to generate new (and better) solutions for evaluation. Thus GAs can improve the quality of decision making.


What is an artificial neural network?
An artificial neural network is a system based on the operation of biological neural networks, in other words, is an emulation of biological neural system. Why would be necessary the implementation of artificial neural networks? Although computing these days is truly advanced, there are certain tasks that a program made for a common microprocessor is unable to perform; even so a software implementation of a neural network can be made with their advantages and disadvantages.
Advantages:
• A neural network can perform tasks that a linear program can not.
• When an element of the neural network fails, it can continue without any problem by their parallel nature.
• A neural network learns and does not need to be reprogrammed.
• It can be implemented in any application.
• It can be implemented without any problem.

Disadvantages:
• The neural network needs training to operate.
• The architecture of a neural network is different from the architecture of microprocessors therefore needs to be emulated.
• Requires high processing time for large neural networks.
Another aspect of the artificial neural networks is that there are different architectures, which consequently requires different types of algorithms, but despite to be an apparently complex system, a neural network is relatively simple.
Artificial neural networks are among the newest signal processing technologies nowadays. The field of work is very interdisciplinary, but the explanation I will give you here will be restricted to an engineering perspective.
In the world of engineering, neural networks have two main functions: Pattern classifiers and as non linear adaptive filters. As its biological predecessor, an artificial neural network is an adaptive system. By adaptive, it means that each parameter is changed during its operation and it is deployed for solving the problem in matter. This is called the training phase.
A artificial neural network is developed with a systematic step-by-step procedure which optimizes a criterion commonly known as the learning rule. The input/output training data is fundamental for these networks as it conveys the information which is necessary to discover the optimal operating point. In addition, a non linear nature make neural network processing elements a very flexible system.

Basically, an artificial neural network is a system. A system is a structure that receives an input, process the data, and provides an output. Commonly, the input consists in a data array which can be anything such as data from an image file, a WAVE sound or any kind of data that can be represented in an array. Once an input is presented to the neural network, and a corresponding desired or target response is set at the output, an error is composed from the difference of the desired response and the real system output.
The error information is fed back to the system which makes all adjustments to their parameters in a systematic fashion (commonly known as the learning rule). This process is repeated until the desired output is acceptable. It is important to notice that the performance hinges heavily on the data. Hence, this is why this data should pre-process with third party algorithms such as DSP algorithms.
In neural network design, the engineer or designer chooses the network topology, the trigger function or performance function, learning rule and the criteria for stopping the training phase. So, it is pretty difficult determining the size and parameters of the network as there is no rule or formula to do it. The best we can do for having success with our design is playing with it. The problem with this method is when the system does not work properly it is hard to refine the solution. Despite this issue, neural networks based solution is very efficient in terms of development, time and resources. By experience, I can tell that artificial neural networks provide real solutions that are difficult to match with other technologies.
Fifteen years ago, Denker said: “artificial neural networks are the second best way to implement a solution” this motivated by their simplicity, design and universality. Nowadays, neural network technologies are emerging as the technology choice for many applications, such as patter recognition, prediction, system identification and control.

3. What are integrated software applications? Discuss their advantages and business utility. “Every component of a computer such as hardware software and network should be protected”. Justify and elaborate.
Integrated software combines the functions of several programs into one interface. Such a program is usually designed for the beginning or casual user.
Many advanced features are omitted that might be found in stand-alone products. For example, the word processor in an integrated software package would not likely have a way to automatically generate a table of contents and would have fewer options on how to handle footnotes, headers, and footers.
Microsoft Works is an example of integrated software.

Suites
A software suite is a set of applications which can each stand alone. They are packaged together for a lower price than if all were bought separately. There are usually enhancements that help the applications communicate with each other, also.
Some popular suites of office software include MS Office, Lotus Smart Suite, WordPerfect Office, OpenOffice, and iWork (for Mac computers). These include several programs, such as:
• word processing
• spreadsheet
• presentations
• e-mail client
• address book
• database
Other Examples of Software Suites
An example of a graphics suite is Corel Draw Suite, which includes programs for:
• vector illustration
• layout
• bitmap creation
• image-editing
• painting
• animation software
An example of a publishing suite is Adobe Publishing Collections, which includes:
• Adobe PageMaker - desktop publishing
• Adobe Photoshop - bitmap graphics
• Adobe Illustrator - vector graphics
• Adobe Acrobat - converts documents to Portable Document Format for sharing
Purpose: Linking different applications together for better work
(integrated software) Simplifying things for new users
Major Advantage: Costs less than a set of similar stand alone applications bought separately
Major Disadvantage: (integrated software) Lacks many features of the stand-alone versions of the same software.

(suites) You may not need all the parts or the advanced features in the collection.

Integrated software is a single program that contains "modules" or "tools" to complete many popular business applications. These applications include word processing, spreadsheets, database management, graphics, and communications.
These tools are sufficient for the typical tasks performed by a small business, a student, or a home user. The word processing module might be used to type a letter or report. The spreadsheet module might be used to do financial analysis or to record comparisons. The database module can be used in a variety of ways, such as to organize an inventory; to compile a list of customers' (or friends') names, street or e-mail addresses, and phone and fax numbers; and to maintain a household inventory for insurance purposes. A graphics module can give an individual or a business an edge by providing tools that will give a "professional look" to documents produced.

One of the reasons integrated software is popular in the education world is that the user can easily switch from one type of application to another without exiting the program. In a beginning or an introductory class or curriculum, the use of integrated software is beneficial because a teacher can quickly develop